University of Southern California Dissertations and Theses

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Health-related quality of life correlates of the treatment decision making process of newly diagnosed prostate cancer patients

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Content HEALTH-RELATED QUALITY OF LIFE CORRELATES OF THE
TREATMENT DECISION MAKING PROCESS OF
NEWLY DIAGNOSED PROSTATE CANCER PATIENTS

by
Antoinette Stephanie Giedzinska-Simons

____________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)

August 2007

Copyright 2007 Antoinette Stephanie Giedzinska-Simons

ii
Acknowledgments
I thank David Agus, MD., Mitchell Gross, MD., Ph.D. and the entire staff at
the Louis Warshaw Prostate Cancer Center at Cedars Sinai Medical Center for
allowing me access to their patients and their clinic, and for providing me with
tremendous support to complete this project. I also thank Drs. Steven Tucker and
Michael Steinberg for their many patient referrals. Most importantly, however, I am
indebted to the prostate cancer patients who participated in this study. To take the
time to complete these questionnaires during a busy and often stressful period in
their lives, speaks volumes about their enthusiasm and desire, not only to support
behavioral medicine research, but to “give something back” to the many other men
who will be diagnosed with prostate cancer in future.
My greatest thanks go to my advisor and mentor, Beth Meyerowitz, Ph.D.
She provided me with the utmost support throughout my graduate training, taught me
to think about and process through the tough questions, and was always the epitome
of a great role model. I look forward to our many years of collaboration and
friendship.
I also thank Kysa Christie, M.A. for joining the project during her first year
in graduate school. I so appreciated her unending assistance, contributions, and
enthusiasm for the POP study. Without Kysa, the study could have not maintained its
momentum and completion.
iii
I give my deepest thanks to my mom, Joanna, whose support never faltered.
My one regret is that you could only join me in spirit to celebrate the completion of
my education. And lastly, thanks go to my husband and friend, Tom. He has stood by
me throughout this process, supported me, and was my greatest cheerleader. I do not
know how I could have completed the task of conducting long-distance research,
raising our beautiful son, and maintaining a sense of humor without him.

iv
Table of Contents

Acknowledgements....................................................................................... ii
List of Tables................................................................................................. vi
List of Figures ............................................................................................... x
Abstract ......................................................................................................... xi
Introduction................................................................................................... 1
Background: The Treatment Decision Making Process .............................. 9
Treatment Information Communication...........................................9
Treatment Choice Deliberation......................................................... 13
Treatment Choice Appraisal ............................................................. 20
Statement of Research Purpose..................................................................... 24
Method .......................................................................................................... 27
Participants........................................................................................27
Procedures.........................................................................................30

Materials........................................................................................................ 33
Demographic and Medical Variables................................................ 34
Exploratory Factor Analyses............................................................. 35
Predictor Variables............................................................................38
Criterion Variables............................................................................50

Data Analysis ................................................................................................ 54
Data Inspection.................................................................................. 54
Statistical Controls............................................................................55
Main Analyses...................................................................................56
Power................................................................................................56

v
Results........................................................................................................... 57
Attrition and Retention...................................................................... 57
Descriptive Findings.........................................................................61
Predictive Findings...........................................................................78
Exploratory Findings.........................................................................112

Discussion ..................................................................................................... 151

Study Limitations.......................................................................................... 170
Conclusions................................................................................................... 178
References..................................................................................................... 180
Appendices
Appendix A.......................................................................................195
Appendix B.......................................................................................197
Appendix C.1....................................................................................199
Appendix C.2....................................................................................204
Appendix D.......................................................................................205
Appendix E.1....................................................................................206
Appendix E.2....................................................................................208
Appendix E.3....................................................................................209
Appendix F........................................................................................210
Appendix G.1....................................................................................211
Appendix G.2....................................................................................212

vi
List of Tables

Table 1: Means and Frequencies of the Medical and Demographic 59
Characteristics of the Participants, Drop-Outs, and Non-
Participants

Table 2: Intercorrelations between Demographic and Pretreatment Medical 62
Variables

Table 3: Summary of Analyses of Variance, Means, and Standard 64
Deviations comparing Treatment Groups on Medical and
Demographic Variables

Table 4: Means, Standard Deviations, and Ranges of Pretreatment 65
Measures of the Treatment Decision Making Process

Table 5: Intercorrelations between Measures of the Treatment Decision 66
Making Process at Pretreatment

Table 6: Correlations between Demographic/Medical Variables and 67
Pretreatment Predictor Measures

Table 7: Comparisons of Means and Standard Deviations of Pre- and 74
One-Month Post-Treatment Health-Related Quality of Life
Measures

Table 8: Correlations between Demographic/Medical Variables and 75
One-Month Post-Treatment Dependent Criterion Measures

Table 9: Frequencies of Bother, and Means and Standard Deviations of 77
the Prostate Cancer Index (PCI) at one-Month Post-Treatment

Table 10: Partial Correlations of Demographic/Medical Co-Variates 80
and One-Month Post-Treatment Criterion Variables

Table 11: Correlations of Measures of Pretreatment Information 82
Communication and Pre- and One-Month Post-Treatment
Health-Related Quality of Life

vii
Table 12a: Summary of Hierarchical Regression Analyses for 83
Pretreatment Information Communication, controlling for
Pretreatment Quality of Life, Predicting FACT-G Summary
and Subscale Measures of One-Month Post-Treatment Quality
of Life

Table 12b: Summary of Hierarchical Regression Analyses for 85
Pretreatment Information Communication, controlling for
Pretreatment Prostate Cancer Quality of Life, Predicting
FACT-P and PCI Measures of One-Month Post- Treatment
Prostate Cancer Quality of Life

Table 12c: Summary of Hierarchical Regression Analyses for 87
Pretreatment Information Communication, controlling for
Pretreatment Psychosocial Adjustment, Predicting One-
Month Post-Treatment Psychosocial Adjustment

Table 13: Correlations of Measures of Pretreatment Choice 91
Deliberation and One-Month Post-Treatment Health-
Related Quality of Life

Table 14a: Summary of Hierarchical Regression Analyses of 93
Pretreatment Choice Deliberation, controlling for
Pretreatment Quality of Life, Treatment Information
Communication and Problem-Focused Coping Predicting
FACT-G Summary and Subscale Measures of One-Month
Post-Treatment Quality of Life

Table 14b: Summary of Hierarchical Regression Analyses of 95
Pretreatment Choice Deliberation, controlling for
Pretreatment Prostate Cancer Quality of Life, Treatment
Information Communication and Problem-Focused Coping
Predicting FACT-P and PCI Measures of One-Month
Post-Treatment Prostate Cancer Quality of Life

Table 14c: Summary of Hierarchical Regression Analyses of 97
Pretreatment Choice Deliberation, controlling for
Pretreatment Psychological Adjustment, Treatment
Information Communication and Problem-Focused
Coping Predicting One-Month Post-Treatment
Psychological Adjustment

viii
Table 15: Summary of Linear Regression Analyses for Consonance 102
in Treatment Decision Making Predicting Satisfaction with
Treatment Choice at Pretreatment and One-Month Post-
Treatment

Table 16: Correlations of Measures of Pretreatment Choice 104
Appraisal and Post-Treatment Health-Related Quality of Life

Table 17a: Summary of Hierarchical Regression Analyses for 106
Variables of Treatment-Specific Optimism, Controlling
for Pretreatment Quality of Life, Predicting FACT-G
Summary and Subscale Measures of One-Month Post-
Treatment Quality of Life

Table 17b: Summary of Hierarchical Regression Analyses for 108
Variables of Treatment-Specific Optimism, Controlling
for Pretreatment Prostate Cancer Quality of Life, Predicting
FACT-P and PCI Measures of One-Month Post-Treatment
Prostate Cancer Quality of Life

Table 17c: Summary of Hierarchical Regression Analyses for 110
Variables of Treatment-Specific Optimism, Controlling
for Pretreatment Psychosocial Adjustment, Predicting One-
Month Post-Treatment Psychosocial Adjustment

Table 18a: Summary of Hierarchical Regression Analyses for 113
Variables of Treatment-Specific Optimism, Controlling
for Pretreatment Dispositional Optimism and General Quality
of Life, Predicting FACT-G Summary and Subscale Measures
One-Month Post-Treatment General Quality of Life

Table 18b: Summary of Hierarchical Regression Analyses for 115
Variables of Treatment-Specific Optimism, Controlling
for Pretreatment Dispositional Optimism and Prostate
Cancer Well-being, Predicting FACT-P and PCI Measures
of One-Month Post-Treatment Prostate Cancer Well-being

Table 18c: Summary of Hierarchical Regression Analyses for 117
Variables of Treatment-Specific Optimism, Controlling
for Pretreatment Dispositional Optimism and Psychosocial
Adjustment, Predicting One-Month Post-Treatment
Psychosocial Adjustment
ix

Table 19: Partial Correlations of Demographic/Medical Co-Variates 121
and Pretreatment Predictor Variables

Table 20: Correlations of Demographic/Medical Variables and 128
Pretreatment Predictor and Post-Treatment Criterion
Measures by Treatment Modality

Table 21a: Correlations of Measures of the Treatment Decision 139
Making Process and One-Month Post-Treatment Health-
Related Quality of Life, among Prostatectomy patients

Table 21b: Correlations of Measures of the Treatment Decision 140
Making Process and One-Month Post-Treatment Health-
Related Quality of Life, among Radiation patients

Table 21c: Correlations of Measures of the Treatment Decision 141
Making Process and One-Month Post-Treatment Health-
Related Quality of Life, among Hormone patients

Table 22: List of Studies Conducted regarding Processes of 171
Treatment Decision Making and Patient Quality of Life

x
List of Figures
Figure 1: Flow Chart of Participant Accrual and Questionnaire 29
Returns

Figure 2: Means for the Functional Assessment of Cancer Therapy 73
(FACT) between POP Study Patients, FACT General
Population, and FACT Cancer Patients

Figure 3: Means Scores for Measures of the Choice Deliberation 124
Process by Medical Characteristics and Age

Figure 4: Means Scores for Measures of Treatment Information 125
Communication by Medical Characteristics and Age

Figure 5: Means Scores for Measures of the Treatment Choice 126
Appraisal by Medical Characteristics and Age

Figure 6: Interactions of Treatment Modality and Age on Patient 133
Appraisal of Physician Communication Effectiveness

Figure 7: Interactions of Treatment Modality and Age on Factors 134
Influencing Treatment Choice

Figure 8: Interactions of Treatment Modality and PSA on Involvement 136
in Treatment Decision Making

Figure 9: Interactions of Treatment Modality and PSA on Satisfaction 137
with Treatment Decision Making Involvement

Figure 10: Interactions of Treatment Modality and Involvement in 146
Treatment Decision Making (TDM) on Functional Well-Being

Figure 11: Interactions of Treatment Modality and Involvement in 147
Treatment Decision Making (TDM) on Physical Well-Being

Figure 12: Interactions of Treatment Modality and Involvement in 148
Treatment Decision Making (TDM) on Prostate Cancer
Well-Being

Figure 13: Interactions of Treatment Modality and Involvement in 150
Treatment Decision Making (TDM) on Negative Mood
xi
Abstract
Research indicates that post-treatment health-related quality of life (HRQL)
of cancer patients may be enhanced when patients participate in their own treatment
decision making (TDM) process. This conception is based on a short history of
research that has operationalized and has measured patient TDM often without the
guidance of an accepted theoretical framework. The current study set out to
operationalize and to design measures of the patient TDM process based on informed
decision making theory comprising three measurable aspects of treatment
information communication, treatment choice deliberation, and treatment choice
appraisal. Exploratory factor analyses demonstrated that these measures possess
good factor structures and internal reliability. In this study, measures of the TDM
process and HRQL of 57 prostate cancer patients were examined prior to primary
treatment and HRQL was examined again at one-month post-treatment, in order to
determine whether patient participation in the TDM process would predict
improvement to HRQL. Residualized hierarchical regression analyses revealed that
the three aspects of the patient TDM process predicted few changes in HRQL
outcomes. Despite the lack of support for the benefits of patient participation in
TDM, the prostate cancer patients in this study reported having ample treatment
information, being very involved in their treatment decision making, being very
satisfied with their treatment choices, and having positive expectations about
treatment outcomes. The men also reported having good HRQL at both time points.
xii
Post-hoc exploratory correlational and moderational analyses indicated that in some
instances TDM participation may be a function of patients’ treatment modality, age,
or their disease severity. Implications for further research are discussed.
1
Prostate cancer is the most common male cancer after skin cancer in the
United States, affecting 1 in 6 men. The American Cancer Society has estimated that
nearly 219,000 men will be diagnosed with prostate cancer in the United States in
2007 (American Cancer Society, 2007). Since the late 1980s, the incident rate of
prostate cancer has increased, although the mortality rates have declined since the
early 1990s; this decline is primarily due to medical intervention at early detection
and the fact that many tumors have a slow growth rate (National Cancer Institute,
2006). Treatments for prostate cancer vary, and in many cases, patients learn that
their cancer can be effectively treated by more than one modality. But according to
Lu-Yao and Yao (1997), there is no consensus regarding the relative survival
benefits of the varying treatments for prostate cancer per stage of disease. However,
there is a consensus that all prostate cancer treatments host a range of adverse side
effects often resulting in post-treatment loss or limited function affecting men’s
quality of life (Litwin et al., 1998; Newton, Burney, Millar, Frydenberg, & Ng,
2006). On the one hand, prostate cancer patients can expect that their chosen
treatment will effectively cure or manage their disease, yet on the other, patients can
expect that their treatment will have some negative impact on their quality of life
(Penson, Litwin, & Aaronson, 2003). Given these outcomes, it is quite likely that
prostate cancer patients will have to face making complex and difficult decisions
about which treatment is best for them.
2
Several accepted primary treatment modalities to treat prostate cancer are
available including radical prostatectomy, radiotherapy, androgen deprivation
therapy, and expectant management. Depending on the stage of disease and patient
medical characteristics, more than one form of treatment may be possible. For
instance, radical prostatectomy and radiotherapy are both available treatments when
cancer is clinically localized or non-metastatic. Radical prostatectomy entails
surgical removal of the prostate gland and often its surrounding environs (Botswick,
MacLennan, & Larson, 1999; Kunkel, Bakker, Myers, Oyesanmi, & Gomella, 2000),
typically resulting in immediate adverse side effects associated with, but not limited
to, urinary incontinence and impotence (O’Rourke, 2001). Radiotherapy requires
patients to have daily clinic visits for up to 8 weeks (Kunkel et al., 2000).
Gastrointestinal and bowel dysfunction are common adverse side effects (O’Rourke,
2001; Pickett, Watkins-Bruner, Joseph, & Burggraf, 2000) resulting in symptoms
such as diarrhea or rectal bleeding; additionally, sexual dysfunction can occur
progressively over time in some radiotherapy patients (Kunkel et al., 2000; Pickett et
al., 2000). Androgen deprivation therapy (ADT), a form of hormone treatment, may
be the treatment of choice for men with advancing prostate cancer; however, ADT is
increasingly being used in combination with other modalities before, during, or after
treatment for localized disease as well (Botswick et al., 1999). ADT reduces the
production of testosterone in the system in order to restrict tumor growth, yet its side
effects are associated with loss of libido, impotence, and “femininizing” changes to
3
the body (Kunkel et al., 2000). Expectant management (often referred to as
"watchful waiting") is usually recommended for advanced-aged men who have other
co-morbidities, have concerns or limitations regarding aggressive cancer treatment,
or have an early detected prostate cancer that is slow growing. Expectant
management for patients usually entails vigilant follow-up monitoring, such as blood
tests and physical exams every six months (O’Rourke, 2001) and can result in
adverse side effects such as urinary incontinence, fatigue, or impotence as the
disease progresses. Psychological difficulties may occur readily for some expectant
management patients due to experiencing uncertainty and anxiety for not actively
treating their disease (Litwin et al., 1995). Choosing any prostate cancer treatment,
or choosing not to have treatment, presents its own morbidity concerns directly
affecting patient quality of life. Quality of life concerns are not only important prior
to treatment as part of the treatment choice process, but are especially important after
treatment, when men are living with and adjusting physically and psychologically to
cancer treatment outcomes.
The concept of quality of life has been widely discussed in the health
literature and has become a central concern for patients and physicians. In 1998 the
World Health Organization (WHO), expanded the concept of quality of life to
signify "health-related quality of life" as a multidimensional construct that organizes
components of health and well-being. Four sub-categories comprise health-related
quality of life: (1) functional status, including physical activities, mobility, work or
4
household responsibilities; (2) disease and/or treatment related symptoms, including
pain, fatigue, nausea, impotence; (3) psychological or emotional well-being,
including anxiety, depression, positive affect; and (4) social functioning, including
one’s ability to engage in social relationships and intimacy (WHO, 1998). Health-
related quality of life is measured most often through patients’ subjective sense of
well-being regarding the effects of their illness and its subsequent treatments as they
adapt to living with cancer (Schipper, Clinch, & Powell, 1990). In prostate cancer
research, health-related quality of life outcomes have been measured extensively in
order to provide information concerning how the various treatment modalities affect
patients, especially in areas of physical functioning (Kunkel et al., 2000; Litwin et
al., 1995; Netwon et al., 2006) psychological issues such as depression and mood
(Kunkel et al., 2000; Litwin et al., 1995), and stress associated with post-treatment
adjustment (Joseph, Thibault, & Ruttle-King, 2006). Such research provides prostate
cancer patients with general information on what to expect concerning their post-
treatment quality of life in their efforts to make treatment decisions.
However, choosing one’s treatment for prostate cancer can be complex.
Given the absence of consensus for an optimal treatment strategy, physicians – and
patients alike – must consider a myriad of factors and treatment information when
considering treatment options. For the consulting or treating oncologist, factors such
as patient age, expected life span, stage and grade of tumor, other co-morbidities, and
patient preferences (Pickett et al., 2000) constitute medical treatment decision
5
making and are discussed with patients as part of the informed consent process. For
the prostate cancer patient, factors that may affect treatment decisions vary from
patient to patient, but are reported to comprise information concerning treatment
efficacy, risk of potential side effects, concerns over economic consequences,
physician and family recommendations, cultural values, or emotions and attitudes
concerning having cancer (Luce, 2005; Zeliadt et al., 2006). With respect to the
current medical paradigm of patient-centered medicine and informed consent, it is
expected that patients and their physicians will discuss the pros and cons of treatment
options within the context of patients’ values, and their expectations of treatment
outcomes and quality of life. It is likely then that prostate cancer patients will be
participating to some extent in their own treatment decision making process.
Because the culture of patient-centered medicine encourages medical
professionals to include patients in the process of making their treatment choices
(Weiss Roberts, 2002), to investigate how cancer patients’ participation in the
treatment decision making process may relate to patient quality of life has clinical
merit. For instance, cancer treatment can be a serious life-changing event and the
process of choosing that treatment becomes complex when decision makers must
consider many factors. Furthermore, decision making itself can be stressful (Steginga
& Occhipinti, 2006) further exacerbating existing patient stress from having been
given a cancer diagnosis (Balneaves & Long, 1999; Luce, 2005; Nelson, Stefanek,
McCaul, & Peters, 2005; Schaeffer, 1989). Thus, the interest to study cancer patient
6
participation in the treatment decision making process is increasing (McCaul,
Nelson, Stefanek, & Peters, 2005; Nelson et al., 2005) in order to identify how
patient characteristics, preferences, and behaviors associated with treatment decision
making participation, prior to cancer treatment, are protective of or harmful to post-
treatment quality of life.
However, studying patient participation in the treatment decision making
process is not without its challenges. Even though formal decision making theory has
been a widely studied construct during the past century in the fields of economics,
statistics, and social and cognitive psychology, research on patient treatment decision
making participation has been presented in the absence of accepted theories or any
established operationalization of “patient treatment decision making.” It is typical in
much of the literature to conceive patient participation in the treatment decision
making process categorically and to evaluate it as a form of passive, shared, or active
participation. This conceptualization is based mostly on patient report on nominal or
ordinal lists of decision making preferences (Beaver et al., 1996; Elit et al., 2003;
Mansell, Poses, Kazis, & Duefield, 2000; Steginga & Occhipinti, 2002; Wong et al.,
2000). Such an approach oversimplifies, thus limits appreciation for, the multitude of
decisional processes in which patients might engage.
Not until very recently have others discussed the importance of studying
cancer patient treatment decision making within the framework of traditional
decision making theories (Luce, 2005; Nelson et al., 2005; McCaul et al., 2005;
7
Rimer, Briss, Zeller, Chan, & Woolf, 2004; Siminoff & Step, 2005). Essentially, this
framework entails that (1) a problem must be solved, (2) information to solve the
problem must be gathered, (3) deliberating over choice options must take place, and
(4) feedback concerning choice is considered. Yet these authors also note that some
of the basic assumptions of classical “normative” theories (that decision makers are
“rational” people who have well articulated values and preferences to guide their
decisions, and who will make good decisions if they just think long and hard about
them) do not serve to explain realistically the decision making experience of having
to choose one’s cancer treatment. Other factors that cancer patients may face during
treatment decision making may include patients experiencing cancer-related stress,
being in a state of information overload, being bombarded with treatment options,
needing to include the treatment opinions of others, and having to process
information that may be difficult to understand (Frosch & Kaplan, 1999; Luce, 2005;
Fischhoff, 2005; McCaul et al., 2005; Siminoff et al., 2005). To integrate these
special circumstances experienced by cancer patients within the framework set forth
by traditional models, several authors have recently presented theories specific to
cancer patient treatment decision making (Elwyn, Edwards, Eccles, & Rovner, 2001;
Rimer et al., 2004, Petersen, Heesaker, & de Witt Marsh, 2001; Siminoff & Step,
2005).
One model, informed decision making (Grady et al., 1995; Rimer et al., 2004;
Rimer, Keintz, Kessler, Engstrom, & Rosan, 1989), blends some of the features
8
unique to cancer patients as definitive aspects of the patient treatment decision
making process. According to Rimer et al. (2004), informed decision making is
hypothesized to occur when a patient receives all the necessary information,
understands the risks and benefits of the procedure, and makes the decision based on
personal preferences. Informed decisions are made through the logical processes of
normative decision making such that (1) the necessary information required to make
treatment decisions is acquired through the informed consent process by which
physicians and patients discuss the diagnosis and treatment options; (2) the
deliberation over treatment choice options entails patients needing to consider and to
deliberate over the risks and benefits of each treatment option within the context of
their values and expectations for quality of life outcomes; and (3) appraisal of the
chosen treatment is made in terms of patient reported satisfaction (Clark & Talcott,
2006). The informed decision making model also is concerned primarily with the
treatment decision making experience of the cancer patient, unlike its contemporaries
that further consider the complexities of the dynamics of other decision makers
(Revenson & Pranikoff, 2005; Siminoff & Step, 2005) or of shared responsibility in
treatment choices (Barry, 2002; Elwyin et al., 2001; Frosch & Kaplan, 1999). Thus
the informed decision making model, with its focus on the patient perspective of the
treatment decision making experience, corresponded with the objectives of the
current study aimed to understand the experiences of prostate cancer patients during
the treatment decision making process and how treatment decision making is related
9
to post-treatment health-related quality of life. The informed decision making model
thus served as the basis from which this current study’s objectives and findings were
organized and explained.
The Treatment Decision Making Process
Given the likelihood that prostate cancer patients will be involved to some
extent in their treatment decision making, conceptualizing their experience through
the framework of informed decision making may be a suitable approach to
understand the myriad of treatment decision making issues prostate cancer patients
may face. In order to address these complex issues within the objectives of the
current study, the three general processes outlined above (Rimer et al., 2004) were
modified more succinctly to comprise three general treatment decision making
processes of prostate cancer patients: (1) treatment information communication, (2)
treatment choice deliberation, and (3) treatment choice appraisal. The following
section will discuss these processes in more detail and will describe how findings
from previous research on treatment decision making have been shown to relate to
patient health-related quality of life.
Treatment Information Communication
One of the main assumptions in decision making theory is that effective
decision making cannot occur in the absence of information. And in terms of the
cancer patient’s experience, the communication of treatment information is an event
that generally precedes the process of choosing a treatment. Initially, physicians
10
engage their patients in the informed consent process that entails informing patients
about what treatment options are available based on physician understanding of
patient medical characteristics (Deber, Kraetschmer, & Irvine, 1996).
Communication exchange then may take place to elicit patient understanding of
treatment information (Rimer et al., 2004) and to provide opportunities for patients
to express their concerns of treatment side effects or preferences for health-related
quality of life outcomes as they relate to each treatment option (Angelos, 2000;
White, Muss, Michielutte, Cooper, & Jackson, 1984). This feedback provides
physicians with more information with which to discuss treatment options further.
Thus the treatment information communication process may be conceived as one that
embodies (1) quantity of information and (2) quality of information. Specifically,
quantity of information from the patient’s perspective may refer to physicians
informing patients of their diagnoses, treatment options, and prognoses, and the
quality of information may refer to how effective the communication is between
physicians and patients. The latter is contingent upon both parties being able to
communicate effectively and to understand what is being communicated.
Communicating treatment information is a dynamic and complex event
(Guadagnoli & Ward, 1998) and has received much attention in the medical
literature with regard to how treatment information affects patient health-related
quality of life. The general lore in the medical literature is that providing patients
with treatment information and talking to them about it are generally accepted as
11
beneficial. Given the uncertainty that a disease diagnosis and its treatments can elicit,
pretreatment information may serve to prepare patients for what to expect regarding
changes to their post-treatment health states (Cassileth, Zupkis, Sutton-Smith, &
March, 1980; Wong et al., 2000). For instance, Sainio, Eriksson, & Lauri (2001)
found that when given accurate, reliable and realistic treatment information prior to
their therapy, cancer patients felt prepared for what to expect after their treatment.
Other studies have shown that treatment information is associated with positive
mood and optimism among prostate cancer patients (Joseph et al., 2006; Wong et al.,
2000), satisfaction with both treatment and physicians among breast cancer patients
(Liang et al., 2002), and increased pain tolerance in surgical patients during
postoperative periods (Vernon & Bigelow, 1974; Wolfer & Visintainer, 1975). Wong
et al. (2000) observed that prostate cancer patients who reported having more
psychological distress (i.e., depression, negative mood, and pessimism) requested
more treatment-related information. Likewise, Joseph et al. (2006) found that
prostate cancer patients who reported having not received sufficient treatment
information also reported having higher degrees of stress at post-treatment. In terms
of quality of information, Liang et al. (2002) found a direct relationship between
breast cancer patients’ satisfaction with their care and the extent to which oncologists
communicated treatment information with patients. In an early review, Stewart
(1995) found that most studies indicated that “effective” communication between
physicians and patients had positive influences on patient emotional health,
12
functional well being, and pain control. These findings suggest that patients’ post-
treatment quality of life may be enhanced by receiving and discussing treatment
information prior to treatment.
However, at least two methodological problems can be identified in much of
this work that affects the generalization of findings: First, many studies assessed
pretreatment information retrospectively after treatment, thus drawing into question
the accuracy of patient recollection (Snyder & Uranowitz, 1978) and casting doubt
upon general claims that pretreatment information is beneficial to later patient
quality of life. In the current study we aimed to remedy this issue by capturing
patient report of treatment information communication prospectively, during the
pretreatment period and assessing quality of life after treatment. Second, many
studies do not distinguish between quantity of information and quality of
information, thus it is not clear whether patients’ quality of life is associated with the
amount of information they received, how well it is communicated, or both. Rimer et
al. (2004) emphasized that differentiating treatment information communication is
warranted because in order for patients to make informed decisions they must
understand the treatment information they are given. Therefore we sought to measure
treatment information communication to comprise patient report of the quantity of
information received and the subjective aspects of that information. The subjective
aspects of treatment information comprise patients’ report of their perceived
understanding of treatment information and of the extent to which they perceived
13
themselves and their physicians to communicate treatment-related information
effectively. Assessing prostate cancer patients’ treatment information
communication in this manner at pretreatment allowed for the opportunity to observe
and to describe the varying aspects of treatment information communication and how
these might relate to post-treatment health-related quality of life.
Treatment Choice Deliberation
Fundamentally, decision making is a human activity encompassing particular
cognitive, analytical processes used to select among choices in light of their possible
consequences. A decision to be made is dictated when more than one plausible
alternative is possible to solve a problem (Edwards, 1954), thus resulting in a
“decisional conflict” requiring one to engage in decision making (Ajzen, 1996). With
respect to cancer patients, and prostate patients specifically, decisional conflicts
occur when more than one treatment alternative is possible to cure or to manage the
cancer (or with expectant management patients, decisional conflict can be
appreciated in whether patients do or do not choose treatment). Each treatment
alternative also presents its own possible morbidity issues that can complicate further
the decisional process. Patients, like all decision makers, will choose a treatment that
will produce the maximal benefit at the minimal cost (Kahneman & Tversky, 1973;
Edwards, 1954). Thus the act of choosing treatment is just one aspect of the
treatment choice deliberation process; factors related to patient values and their
expectations for quality of life will have varying degrees of influence on choice
14
deliberation as well. Therefore, treatment choice deliberation comprises at least two
aspects, that of patient decision making behaviors and the extent to which decisions
are influenced by various factors.
Factors that cancer patients consider important or influential in their
treatment decision making are generally accepted as those comprising physician
recommendations for treatment (Denmark-Wahnefried et al., 1998; Diefenbach et al.,
2002; O’Rourke, 1999) and specific treatment outcomes (Denmark-Wahnefried et
al., 1998; Henman, Butow, Brown, Boyle, & Tattersall, 2002; O’Rourke, 1999;
O’Rourke & Germino, 1998). In three studies that asked prostate cancer patients to
indicate which factors were the most influential in determining their treatment
choices, over 50% of patients identified their physicians’ opinions to have been the
most influential (Demark-Wahnefried et al., 1998; Hall, Boyd, Lippert, &
Theordorescu, 2003; Miles, Giesler, & Kattan, 1999). With regard to patients’
treatment choices being influenced by treatment side effects, several studies found
that concerns about treatment complications, long recovery, and treatment-related
morbidity also were reported by patients as influential (Holmboe & Concato, 2000;
O’Rourke, 1999; Stenginga & Occhipinti, 2002). Non-medical factors come into
play during treatment decision making, although not as influential. Factors related to
financial or career concerns have been reported as having a small influence on
treatment choice (Crawford et al., 1997; Demark-Wahnefried et al., 1998; Feldman-
Stewart, Brundage, Van Manen, & Svenson, 2004; Holmboe & Concato, 2000) as
15
does advice from family or friends (Denmark-Wahnefried et al., 1998; Diefenbach et
al., 2002; O’Rourke & Germino, 1998). To our knowledge, no data are published
regarding how this component of the choice deliberation process is related to
patients’ actual involvement in making treatment choices. And although we do know
what factors are presumed to be influential in treatment decision making as presented
by these data, we do not know whether influences on treatment decision making
have any meaningful contribution to patient post-treatment quality of life.
After considering treatment information and deliberating over treatment
options and related factors in the context of one’s quality of life expectations, the
patient decision maker must come to a final treatment choice. Yet in treatment
decision making research, this is the one aspect of treatment decision making that
has been researched with the most imprecision. “Treatment decision making” has
been measured more often in terms of treatment decisional control (Davison, Degner,
& Morgan, 1995; Degner & Sloan, 1992; Gattellari, Botow, & Tattersall, 2001),
being offered a treatment choice (Fallowfield, 1997; Morris & Royle, 1988; Siminoff
& Fetting, 1991), and treatment decision making preference (Mansell et al., 2000;
Steginga & Occhipinti, 2002; Wong et al., 2000), where only few have tested
treatment decision making in terms of a traditional decision making process
(Davison et al., 2002; Petersen, Heesacker, Schwartz, & Schwartz , 2000; Petersen et
al., 2001). Despite these inconsistencies in operationalization and in measurement,
much of the research indicates that patients who participate in making treatment
16
decisions are likely to have favorable post-treatment quality of life (Deadman,
Leinster, Owens, Dewey, & Slade, 2001; Hack, Degner, Watson, & Sinha, 2006;
Morris & Ingam, 1988; Morris & Royle, 1988; Moyer & Salovey, 1998).
In a longitudinal study, measuring breast cancer patients’ reports of being
given a choice of cancer treatment at pretreatment and measuring emotional
adjustment at post-treatment over time, Morris and Royle (1988) found that women
who were given a choice reported having lower levels of depression at pretreatment
and at 3 months post-treatment. In a retrospective study of breast cancer patients
(Fallowfied, Hall, Maguire, Baum, & A’Hem, 1994) similar results were found, such
that those who were given a choice in treatment also reported having less depression
at 3 years. Hack et al. (2006) found similar results when measuring breast cancer
patients “participation” in treatment decision making as passive, active, or
collaborative. Those who reported being active in treatment decision making also
were more likely to report having higher quality of life 3 years following treatment,
than either passive or collaborative decision making patients. Similar results for
prostate cancer patients also have been observed: Davison et al. (2002) involved
prostate cancer patients in a computerized treatment decision making program,
measuring their reported degree of activity using the program along with their
reported psychological distress (i.e., depression); patients who actively used the
system to a greater extent, also reported having less depression 4-months after
treatment.
17
On the surface it would appear that patient involvement in treatment decision
making has some benefits to patient post-treatment health-related quality of life. One
reason proposed as to why this may occur has to do with how patients cope with
stress. Because having cancer can be stressful, exercising choice in treatment or
playing an active role in making treatment decisions serves to cope actively with this
stress (Luce, 2005; Petersen et al., 2001), which is thought to buffer against poor
quality of life adjustment (Brody, Miller, Lerman, Smith, & Caputo, 1989). Coping
is defined as the cognitive or behavioral effort put forth by an individual to manage
(either by reducing, minimizing, mastering or tolerating) the demands of a taxing
(i.e., stressful) transaction (Folkman, Lazarus, Gruen, & DeLongis, 1986). The ways
in which people cope with stressful events makes a difference in how they feel
emotionally (Folkman & Lazarus, 1988) and in their quality of life (Bjorck, Hopp, &
Jones, 1999; Sears, Stanton, & Danoff-Burg, 2003; Taylor, Kemeny, Reed, Bower,
& Gruenewald, 2000). This has been shown to be the case among cancer patients
where coping with the stress of being diagnosed with cancer, as well as with the
stress of enduring cancer treatment, is related to psychosocial adjustment or quality
of life (Blank & Bellizzi, 2006; Brody et al., 1989; Carver et al., 1993; Merluzzi &
Sanchez, 1997; Stanton et al., 1998; Truant & Bottorff, 1999). People cope either by
utilizing problem-focused strategies to take active efforts in order to alleviate
stressful circumstances or by utilizing emotion-focused strategies to regulate the
emotional consequences of stress (Folkman et al., 1986). Problem-focused strategies,
18
such as problem solving, seeking support, or positive reframing (Feifel & Strack,
1989; Folkman et al., 1986), are those proposed to be effective when stressors are
perceived to be potentially controllable, whereas emotion-focused strategies are
considered effective when stressors are perceived as uncontrollable (i.e., use of
acceptance, withdrawal, distraction) (Schaeffer, 1989).
Having cancer treatment options that effectively treat or manage the disease
represents a controllable, albeit stressful, situation. It seems likely then, that patients
may utilize problem-focused strategies of coping during the treatment choice
deliberation process. The few studies that examined both coping strategies and
treatment decision making processes have found that cancer patients do tend to use
problem-focused coping in relation to actively participating in treatment choice
deliberation (Petersen et al., 2001) and to rally for control over their treatment
decision making (Truant & Bottorff, 1999). Petersen et al. (2001) aimed to assess
how patients’ cognitive processing during treatment decision making would be
related to their coping strategies. The authors found that depth of cognitive
processing, such as active information seeking and deep information processing, was
associated with greater use of problem-focused coping strategies (i.e., seeking
support and positive reframing) during treatment decision making. What this study
did not indicate, however, was whether treatment choice deliberation was a type of
problem-focused coping, or separate but related process.
19
Further distinguishing the nuances of coping from treatment choice
deliberation may have clinical importance because not all men will use similar
coping strategies to cope with the stress of their cancer (Carver, Scheier, &
Weintraub, 1989; Folkman et al., 1986), yet may likely have to engage in some form
of treatment decision making,. It is expected that most patients will be involved to
some degree in their treatment decision making, and considering this involvement as
one with which patients must cope – rather than considering treatment decision
making involvement as just another way of coping with having cancer – may provide
added clarity in defining the choice deliberation process. We expected, then, to
observe problem-focused coping strategies to be a distinct, yet related construct to
that of choice deliberation.
The choice deliberation process for cancer patients may certainly represent a
multifaceted event. Patients have to consider important factors that may have varying
degrees of influence, as well as having to engage in deliberating and in choosing
their treatment, all while having to cope with the stress of having a cancer diagnosis.
Relying on vague representations of “patient treatment decision making,” as has
been done in previous research, is an underestimation of the complexities of the
choice deliberation process and limits our understanding of what aspects of patients’
experiences during treatment decision making contribute specifically to post-
treatment quality of life. One other oversight identified in much of the treatment
decision making literature is that measures of “treatment decision making” often are
20
not distinguished from those of “treatment information communication” (Deber et
al., 1996; Fallowfield, 2001). This indistinctness in operationalization prevents
establishing a clear appreciation concerning which aspect of the treatment decision
making process is related to patient quality of life. If choice deliberation comprises
events by which treatment choices are (1) influenced by treatment factors and patient
expectations and (2) made through actions of deliberation and of exercising choice,
then measures should be designed with these processes in mind. In the current study,
these issues were addressed by developing measures based on these definitions.
Given the assumption in the literature that patient participation in the treatment
decision making process will predict health-related quality of life outcomes, we set
out to test this hypothesis using measures designed to capture treatment choice
deliberation based on theoretical principles. And unique to the current project, we
expected to observe that measures of patient choice deliberation would relate to post-
treatment quality of life above and beyond treatment information communication and
problem-focused coping.
Treatment Choice Appraisal
The final step of the patient treatment decision making process is concerned
with the feedback of the decision choice. According to traditional decision theory,
decision makers determine whether they are satisfied with the results of their choice,
and if they are not, they must (1) re-enter a new decision process or (2) change their
views about the outcome of their original choice (Ajzen, 1996). With respect to
21
cancer treatment, some treatments are final and not always amenable to change. For
instance, the prostate cancer patient who chose prostatectomy and is dissatisfied with
this treatment cannot re-enter a new treatment decision process once that treatment
has been completed. Patients may not be able to change a treatment if they are not
satisfied with that choice, but can still have positive expectations regarding the
outcome of that treatment; this notion may further expand on an important part of the
final treatment decision making process. Therefore, two potential ways that prostate
cancer patients can appraise their treatment choices may be in terms of satisfaction
and of positive expectations.
Satisfaction. According to Rimer et al. (2004), determining patient
satisfaction with treatment choices and whether patients are satisfied with their
decision making experience is an important undertaking and one that has been given
little attention. Given that healthcare professionals are well aware of the issues of
malpractice and patient dissatisfaction, identifying factors of patient satisfaction as
they relate to patient participation in treatment decision making is of understandable
importance to quality patient care. A growing interest in studying patient satisfaction
with treatment choice and with treatment decision making participation is beginning
to surface in the medical literature. The few studies that have addressed this issue
have found that patient satisfaction with treatment choices or decision making
process is related to good quality of life (Sheriff et al., 2005), less psychological
distress (Bosnjak, Radulovic, Neskovic-Konstantinovic, & Mitrovic, 2000), greater
22
social support (Hoffman, Hunt, Gilliland, Sephenson, & Potosky, 2003), and low
treatment regret (Davison & Goldberg, 2002; Lantz et al., 2005).
One method suggested by some toward improving patient satisfaction is for
physicians to ensure that patients’ participation in the treatment decision making
process be congruent with the level of participation they wish (Davidson, Brundage,
& Feldman-Sterwart, 1999; Ganz, 2002, Gattellari et al., 2001; Hoffman et al., 2003;
Kaplan, 1991; Lantz et al., 2005). This recommendation is based, in part, on the
notion that although the advent of patient-centered medicine speaks to the benefits of
patient treatment decision making participation on post-treatment quality of life, not
all patients will want to be “actively” involved in making their treatment choices
(Ende, Kazis, Ash, & Morkowitz, 1989; Strull, Bernard, & Gerald, 1984). In a
longitudinal study examining the congruency between patients’ preferred treatment
decision making role versus actual role, Gattellari et al. (2001) observed that
congruency in treatment decision making, not the actual role patients played in
treatment decision making, was predictive of patient satisfaction. This finding also
was observed among breast cancer patients (Lantz et al., 2005). Consonance in
treatment decision making, not the degree of patients’ involvement in treatment
decision making, was found to predict breast cancer patients’ satisfaction with their
treatment. In the current study, we expected to observe similar findings. We
predicted that prostate patients who participated in the treatment decision making
process in the manner in which they preferred would be more satisfied with their
23
treatment choice before their primary treatment and would continue to be satisfied
with their treatment choice after treatment.
Expectations. Another form of feedback associated with decision choice is
that of patients having positive expectations, or optimism, regarding their chosen
treatment. Given that patient expectations for quality of life outcomes play an
important role in choosing a treatment, considering the extent to which positive
expectations of patients’ chosen treatment, or “treatment-specific optimism,” relates
to post-treatment quality of life may have merit. Two studies considering this
potential found that treatment-specific optimism in cancer patients was related to
patient psychosocial adjustment. Among breast cancer patients, women with
negative expectations about their chosen treatment demonstrated more emotional
distress at 3 months and 1 year after treatment than did women with positive
treatment expectations (Stanton et al., 1998). And among renal cell cancer patients
enrolled in a Phase 1 clinical trial, those who were optimistic about the treatment at
the beginning of the trial had fewer symptoms of depression at the end of the trial in
comparison to less treatment-optimistic patients (Cohen, de Moor, & Amato, 2001).
The shared strength of these studies, their prospective longitudinal design and having
controlled for measures of emotional distress at pretreatment, substantiates the
possibility that patient treatment optimism may be protective for post-treatment
adjustment. This relationship may be explained by the widely-held hypothesis that
dispositional optimism is a buffer against poor psychosocial and quality of life
24
adjustment, primarily due to the mediational nature of problem-focused coping
strategies (Brissette, Scheier, & Carver, 2002; Carver et al., 1989; Carver et al.,
1993). However, in the Stanton et al. (1998) and Cohen et al. (2001) studies, neither
coping nor dispositional optimism was accounted for. Not including these factors
limits our understanding of whether treatment-specific optimism is an unique
condition of patients’ choice appraisal or a proxy measurement of patients’
dispositional characteristics.
Given the organization of this current study, we took the opportunity to test
whether treatment-specific optimism would yield similar health-related quality of life
benefits for prostate cancer patients. Given the premise that problem-focused coping
confers such benefits to quality of life by mediating dispositional optimism and
health-related quality of life, we expected to find a similar pattern occurring for those
patients who were optimistic about their treatment. In addition, no study to date has
determined whether treatment-specific optimism is distinct from dispositional
optimism, and such an investigation is warranted; we therefore set out to test for this
in an exploratory manner.
Statement of Research Purpose
Ideally, to investigate patient participation in treatment decision making in
the truest sense would require a comprehensive and extensive research program
devoted to such an endeavor (Fischhoff, 2005), and such an effort is beyond the
scope of this current study. However to contribute, on a smaller scale, meaningful
25
data that might add to the current understanding of patients’ experiences in their
treatment decision making process, from the patients’ perspective, was the impetus
of this current work. The aims of this current project were to attempt to provide a
clearer understanding of some of the factors associated with patients’ participation in
the treatment decision making process, and to observe how these factors, in turn,
may relate to patient post-treatment health-related quality of life. Three hypotheses
and two questions were addressed.
Treatment Information Communication
Question 1. How do the aspects of treatment information communication
relate to patient health-related quality of life prior to treatment? And how do these
aspects uniquely or collectively contribute to patient health-related quality of life
after treatment?
Treatment Choice Deliberation
Hypothesis 1. Prostate cancer patients who are actively involved in the
treatment choice deliberation process will report better post-treatment health-related
quality of life than those patients who are not actively involved in this process.
Treatment choice deliberation will contribute uniquely to health-related quality of
life above and beyond that of pretreatment treatment information communication and
patient problem-focused coping strategies.

26
Treatment Choice Appraisal
Hypothesis 2. Prostate cancer patients who engaged in the treatment decision
making process consonant with their own preference will be more satisfied with their
treatment choice before and after primary treatment than will patients whose actual
involvement was disparate from their treatment decision making preferences.
Hypothesis 3. Relationships between treatment-related optimism, problem-
focused coping, and health-related quality of life will be found. Specifically, prostate
cancer patients who are optimistic about their treatment choice before having that
treatment will report better health-related quality of life post-treatment than will
patients who are less optimistic about their treatment choice. This relationship will
be mediated by coping. Treatment-specific optimistic patients will be more likely to
use problem-focused coping strategies than less treatment-specific optimistic
patients, which will lead to better health-related quality of life.
Question 2. Does treatment-specific optimism uniquely account for post-
treatment health-related quality of life factors above and beyond pretreatment
dispositional optimism?
These aims were investigated at two time points of prostate cancer patients
experiences: 1) The time period prior to patients receiving their primary treatment,
thus during the pretreatment phase within which treatment decisions are made, and
2) one month after primary treatment was completed.

27
Method
The current study is part of a larger non-randomized longitudinal study,
known as the Prostate Outcomes Project (POP). POP is an interdisciplinary project
conducted in collaboration with physicians affiliated with Cedars-Sinai Medical
Center (CSMC) in Los Angeles, California. The study was designed to survey
prostate cancer patients prior to their receiving primary treatment and at various time
points post-treatment in order to examine psychological correlates of prostate cancer
treatment decision making and health-related quality of life.
Participants
Participants in the study were recruited from outpatient urology and radiation
clinics staffed by physicians affiliated with CSMC. The majority of patients (89%)
were referred by physicians specializing in medical and surgical oncology (35%),
hormone therapy (34%), and radiation oncology (20%), all of whom are located in
Los Angeles County. The remaining 11% of patients were self-referred, either by
learning of the study through the CSMC website (which advertises all center clinical
research and trials) or by word of mouth.
Inclusion criteria were that patients must have (1) had a first-time prostate
cancer diagnosis, (2) exhibited physical and mental ability to complete questionnaire
packets, and (3) been able to read and to write English. Exclusion criteria were that
patients could not have (1) evidence of prostate cancer metastases, (2) other active
28
cancers other than treated squamous/basal cell carcinoma of the skin, and (3) already
begun primary treatment for their prostate cancer prior to participating in this study.
In all, 162 men were referred to the project over a 27-month time period. Of
these men, 41 did not meet eligibility criteria for the study: Fifteen men were
excluded on the basis of having already begun primary treatment for prostate cancer,
2 men had diagnoses of metastatic disease, and 2 men did not have a confirmed
diagnosis of prostate cancer; another 22 men were considered “ineligible” for this
specific study on the grounds that they already had begun their primary cancer
treatment prior to completing their pretreatment questionnaire. Of a potential sample
of 121 men, 44 declined to participate (25 men explicitly declined participation and
19 men could not be successfully reached and never returned phone messages, thus
these 19 were categorized as “soft declines”). A resulting total of 77 men were
consented to the project and were mailed the pretreatment questionnaire packet.
Seventy-four participants returned their pretreatment questionnaire on time, and of
these, 57 participants returned their one-month post questionnaires on time.
1
Therefore, analyses for this current study were conducted on these 57 participants.
Figure 1 provides a flowchart illustrating participant accrual and questionnaire
distribution for pretreatment and one-month post-treatment.

1
A cut-off time period was determined by allowing for US Post and CSMC mail time delays of up to
7 days.
29
Figure 1
Flow Chart of Participant Accrual and Questionnaire Returns

Patients Referred
N=162
Patients
Eligible
n=121
Patients
Ineligible
n=41
Patients Consented
& Mailed
Pretreatment
Packet
n=77
Patients
Declined
n=44
Patients
Withdrew
n=2
Pretreatment
questionnaires
returned
n=74
Pretreatment
questionnaire not
returned or
completed in time
n=3
One-month post-treatment
questionnaire returned on time
n=57
One-month
questionnaire not
returned or
completed in time
n=15
30
Procedures
All men who met inclusion/exclusion criteria were invited to participate in
the POP study. When a study referring physician determined that a patient was
interested and was eligible to participate, the physician either consented the patient
during an office consult, or contacted the study investigator with the patient’s contact
information so that the investigator could conduct the consent process over the
phone. Self-referred patients contacted the study center independent of physician
referral, and were consented by the study investigator over the phone. Those patients
who declined to participate during the investigator phone contact were asked
permission to respond to a few questions concerning their disinterest, quality of life,
and treatment decision making (Appendix B). Of the 25 men who explicitly declined
participation, 14 (56%) agreed to answer these questions.
Informed consent was achieved with patients either in person or on the
telephone through active discussion of the study’s objectives, expectations of
participants, and timeline. For the phone consent process, patients who continued to
express interest in participating were mailed the consent form to review further, sign,
and return to the study center. The consent was then signed by the consenting
investigator and a copy of the entire document was sent back to the participant for
reference. When the consent process took place during an office consult, both patient
and investigator reviewed the contents of the consent form together, and signed the
consent together at that time.
31
Once informed consent was obtained, patients either were given or mailed the
pretreatment packet that contained the first questionnaire, medical self-history
survey, and a postage-paid envelope. Subsequent questionnaire packets were mailed
directly to participants’ home addresses. For each mailing, patients were instructed to
return their questionnaire packets in the provided, postage-paid envelope within 14
days of receipt. For those who did not return their questionnaire packets within this
time period, study staff contacted patients via phone with a friendly reminder. Up to
three phone attempts were made for each late-return questionnaire, spanning
approximately 5-7 days between calls. If after three phone attempts no questionnaire
packet was returned, the circumstance was noted in the patient file and the patient
was scheduled to receive his next questionnaire in the tracking.
Participating patients consented to participate over a five-year time period
and agreed to complete six (6) quality of life questionnaire packets during the span
of the study. The distribution of questionnaires to patients who had either surgery or
radiation as their primary treatment was as follows: The first questionnaire was to be
completed prior to patients’ primary treatment, the second questionnaire was to be
completed one-month following the last day of patients’ primary treatment, the third
questionnaire was to be completed at six-months following their last day of
treatment, the fourth questionnaire at one-year, the fifth at two-years, and the sixth
questionnaire was to be completed at five-years after the last day of patients’ primary
treatment.
32
The distribution of questionnaires to patients who had hormone therapy as
their primary treatment differed from surgery and radiation patients. The course of
hormone therapy (AKA: androgen deprivation therapy or ADT) can vary from
receiving injections monthly, semi-annually, or annually spanning over 15 months to
several years (NCI, 2006); additionally, the treatment side effects of ADT patients
are not typically experienced immediately after injection. Rather than exclude ADT
patients from POP due to the indeterminate nature of their treatment, it was decided
to include them in order to observe their quality of life and adjustment as compared
to surgery and radiation patients over time. Therefore, for patients receiving hormone
therapy as their primary treatment, their first questionnaire was completed prior to
receiving their first hormone injection, and their second questionnaire was to be
completed one-month following the first day their hormone treatment began;
likewise the scheduled mailing of all subsequent questionnaires were based on the
start date of their ADT treatment (rather than the last day of primary treatment as it
was with surgery and radiation patients).
Patients who chose expectant management to monitor carefully their cancer
over time, comprised a small sample, but also were invited to participate in the study.
They completed the first questionnaire (i.e., the “pre” treatment questionnaire)
immediately after being consented to participate. Subsequent questionnaire packets
(which comprised the “post-treatment” questionnaires) were scheduled for mailing
based on the date these patients returned their pretreatment questionnaire packet (for
33
example, the second questionnaire, the one-month post-treatment questionnaire, was
scheduled for mailing one month after the date we received the first questionnaire
from participants in this group).
Materials
The present study examined data provided by the prostate cancer patients at
the first two time-points: pretreatment (Time 1) and one-month post-treatment (Time
2). The pretreatment questionnaire and post-treatment questionnaire packets
2
were
self-report and included assessments to measure various attitudes and behaviors
associated with prostate cancer patient health, quality of life, psychosocial
adjustment, and experiences with treatment decision making. For the purpose of the
current study, measures evaluated were assessed at pre- and post-treatment, and were
organized as (1) demographic and medical measures, (2) predictor measures of the
treatment decision making process, and (3) criterion measures related to health-
related quality of life outcomes. Appendix A provides a listing of these measures and
when they were evaluated specific to the current study.
Measures evaluated at pretreatment comprised surveys to assess demographic
and medical characteristics that served to describe the sample and served as control
variables in multivariate analyses if they correlated significantly with outcome

2
Essentially, the pre- and post-treatment packets for the larger study comprised the same measures,
however, scales thought to measure information redundantly (i.e., demographics or psychological
traits) were not included in post-treatment questionnaire packets. These were a medical self-history
form, questions on demographics, and a psychological traits scale assessing one’s cognitive
preferences. Scales in the post-treatment questionnaire packets, not included in the pretreatment
questionnaire, were measures of post prostate cancer treatment concerns such as post-treatment
functioning, cancer meaning, and patients’ fears of recurrence.
34
measures. Pretreatment measures evaluated in the current study also were those
related to the treatment decision making process and served to comprise the predictor
variables. Because many predictor measures were developed for the purposes of the
current study, a special section is included describing how measures were developed
and the rules that guided factor and reliability analyses. Measures evaluated at both
pre- and post-treatment were those related to patient health-related quality of life
outcomes and comprised the criterion variables. Criterion measures were evaluated
at both time periods so that (1) they could provide a descriptive account concerning
patient health-related quality of life, (2) they could indicate any change over time to
patient health-related quality of life, and (3) their respective pretreatment measures
could serve as control variables in predictive models concerning their corresponding
post-treatment outcomes. All selected measures are tabled in Appendix A and
organized based on the presentation described above.
Demographic and Medical Variables
The pretreatment questionnaire packet included a medical self-history survey
designed to assess demographic characteristics and medical status. Medical status
measures such as PSA and Gleason scores, representing disease severity, were
relevant to the current study and were assessed at Time 1. PSA is an acronym for a
diagnostic test measuring the prostate-specific antigen (PSA) protein produced by
the cells of the prostate gland, and provides a rough indicator of a biological or
tumor marker. High PSA scores indicate higher levels of PSA in the blood, requiring
35
further testing to rule out the presence of cancer. The Gleason score is derived from a
system of grading prostate cancer tissue based on how it looks under a microscope.
Gleason scores range from 2 to 10 and indicate the severity of cancer, or how likely
it is that a tumor will spread. A low Gleason score indicates that a cancer tissue is
similar to normal prostate tissue and the tumor is less likely to spread; a high
Gleason score indicates that a cancer tissue is very different from normal and the
tumor is more likely to spread. Additional items that were not captured by the self-
history survey were added to the first page of the pretreatment questionnaire; these
were education and income.
Exploratory Factor Analyses
As one of the objectives in this study was to understand patient factors
associated with the treatment decision making process, it was necessary to develop
measurements to address these interests. The current literature lacks an accepted
operationalization of “patient treatment decision making” and no standard or
validated tool to measure this concept has been established. Therefore, one of the
aims of the POP project was to initiate the development of scales to measure various
aspects of the treatment decision making process. Scale development was begun by a
thorough review of the existing treatment decision making literature and then
developed in collaboration with expert prostate oncology staff. Resulting scales then
were piloted through a series of small focus groups comprising prostate cancer
patients treated at CSMC. The resulting scales are organized based on the three
36
processes of the informed decision making framework described previously. Scales
pertaining to “treatment information communication” were designed to capture the
subjective aspects of treatment communication such as patient understanding, patient
communication ability, and patient appraisal of physician communication ability.
Scales pertaining to “treatment choice deliberation” were designed to capture two
distinct processes, specifically, the extent to which factors influenced patients’
treatment choices, and the extent to which patients reported their involvement in the
treatment decision making process. Scales pertaining to “treatment choice appraisal”
were designed to measure patient consonance with their preferred decision making
approach and to measure patient appraisal of their treatment choice. The latter
comprises (1) satisfaction with treatment choice, (2) satisfaction with treatment
decision making involvement, and (3) treatment-specific optimism. All scales were
factor analyzed from the data taken at pretreatment from participants who returned
their pretreatment questionnaire packets prior to their primary treatment (N=74).
Three additional assessments, which have been previously developed, were
conceived as representing parts of the treatment decision making process and were
included as such in this current study. These include the Medical Team
Communication scale (Perez, 2000), which measures the amount of treatment
information communication patients receive. The Brief COPE (Carver, 1997)
measures coping strategies used by patients in dealing with stressful situations and
served to represent part of the treatment choice deliberation process. And the Life
37
Orientation Test (Scheier, Carver, & Bridges, 1994), a widely accepted measure of
dispositional optimism, was included to observe its association with treatment-
specific optimism.
The validity analyses of the current study were considered to be preliminary
because the objectives of the current study were not to establish a formal
operationalization of the treatment decision making process, but to initiate this
process. Therefore, exploratory rather than confirmatory approaches to factor
analyze the data were the chosen approach. Principal component analysis was chosen
as the exploratory technique in order to provide an analysis of how well the variables
of the study designed scales would relate to one another; to reveal any parsimonious
representations of the factor structures in the study designed scales; and to guide
further theoretical development as well as to initiate the process of construct validity
(Nunnally, 1978). Acceptable factor structures observed within each measure were
used to make subscales to describe the sample more specifically regarding nuances
of patient involvement in the treatment decision making process. Reporting the
descriptive findings based on the subscales will provide a more detailed account of
patients’ experiences that may otherwise be overlooked from reporting only
summary data. However for all bivariate and multivariate analyses, only the total
summary score of each measure was used.
Varimax rotation methods were used to extract underlying factors that would
maximize the variance explained by the smallest number of resulting factors. Factor
38
solutions were examined based on the results of scree tests and eigenvalues greater
than 1. Item loadings greater than 0.30 were initially considered to be eligible for
interpretation (Tabachnick & Fidell, 2001); however, items were considered for
deletion if they had a factor loading of less than 0.40 within their own anticipated
factor or loaded greater than 0.45 on two or more components (Comrey, 1973). To
estimate reliability, internal consistency was assessed using Cronbach’s alphas;
scales demonstrating alphas at or above 0.70 were accepted as having adequate
reliability (Cronbach, 1951), and if variables were found to reduce the reliability
coefficient meaningfully, they were subsequently dropped from the scale.
Predictor Variables
Treatment Information Communication
Four measures were developed to comprise the treatment information
communication process. These measures were designed to capture the amount of
communication received and discussed (“communication with medical team”), and
the quality of information received in terms of physicians’ communication
effectiveness (“patient appraisal of physician communication”), patients’
communication effectiveness (“patients’ ability to communicate effectively”), and
patients’ understanding of information (“patient perceived understanding”). These
four measures were expected to positively correlate together, and together represent
that greater amounts of information communicated and received would predict better
health-related quality of life.
39
Quantity of information communication. To measure how much treatment
information communication was experienced by patients, the Communication with
Medical Team Scale (CMT: Perez, 2000) was assessed at pretreatment. Its 10
questions address treatment information concerning patients’ current experience in
obtaining information from their physician or medical team, and are answered on 7-
point Likert-type scales. Items are summed together with higher scores indicating
greater amounts of treatment information obtained. The scale has reported very good
internal consistency (= 0.85) (Perez, 2000); in the present sample alpha reliability
was similar at 0.84.
Quality of information communication. Three additional measures, designed
to capture subjective aspects of treatment information communication, were created
and embedded into the greater Communication with Medical Team scale (See
Appendix C.1). The rationale to embed these scales into the CMT was primarily to
maintain thematic consistency in assessing patients in terms of treatment
information. Additionally, study developed scales relied on the same test instruction
and response anchors as the CMT, so including all scales into one “measure” was a
practical choice. For ease of continued reference in the current report, these scales
are assigned acronyms. Thus the three scales were designed to measure (1) patients’
perceived understanding of treatment information (PPU), (2) patients’ personal
opinion about their own ability to communicate effectively with medical staff
(ACE), and (3) patients’ appraisal of their physician’s (or medical team’s) ability to
40
communicate treatment information effectively (PACE). Four items comprise the
PPU asking patients to rate their understanding of treatment information (2 items),
satisfaction with information (1 item), and preparedness of post-treatment
expectations (1 item). Like the CMT scale, items are answered on 7-point Likert-
type scales, ranging from 1 (no understanding) to 7 (complete understanding)
3
and
summed, with higher scores indicating higher levels of understanding. The ACE and
PACE scales were designed to measure patients’ assessment of communication
ability about themselves and their physicians, respectively. Four items comprise the
ACE asking patients to rate their own ability to obtain treatment information (1
item), to communicate one’s preferences (1 item), to negotiate treatment plans (1
item), and to understand treatment information. The PACE is a five-item scale
asking patients to rate their doctor’s (or medical team’s) ability to communicate
effectively and respectfully. Like certain portions of the CMT, the ACE and PACE
scales are answered on 7-point Likert-type scales measuring ability on both scales
from 1 (poor ability) to 7 (excellent ability). Higher scores represent better patient
perceived communication ability of self (ACE) and of physician (PACE).
Because the three new scales were embedded within CMT scale, principal
components factor analysis was conducted on all items of the four scales combined.
Analyses resulted in a five-factor solution, accounting for 70.5% of the variance. The
majority of items for each of the specific scales clustered together within their own

3
Three of the four items of the PPU are answered in this fashion. The fourth item response is 1 (no
satisfaction) to 7 (extreme satisfaction).
41
apriori domains, yet notable overlap of several items was observed across several
factor components. The one scale with the most overlapping items was the CMT
scale; given that the CMT was previously designed with its factor structure already
reported (Perez, 2000), principal components analysis was re-conducted removing
the CMT from analyses. The resulting factor loadings can be reviewed in Appendix
C.2. Principal components analysis for the three study-specific designed scales
(PPU, ACE, and PACE) resulted in a three-factor solution, accounting for 74.3% of
the variance. Clusters of items delineated into each of the three scales as anticipated
and reliability analyses were conducted on each scale separately.
With respect to patient perceived understanding, one of its items (satisfied
with treatment information) loaded less than 0.40 within its own scale component.
Subsequent review of its face validity lends credence to how “satisfaction” with
treatment information would not load well together with that of treatment
information “understanding;” therefore, this item was dropped from the scale,
resulting in a three-item PPU scale. Cronbach’s alpha estimates further confirmed
the effect of this item; alpha for the four-item scale was below adequate (= 0.67),
whereas for the revised three-item scale, alpha increased to 0.76.
With respect to the patient communication effectiveness, its items loaded
strongly within its own factor component, but also tended to load notably on other
components, and these secondary loadings violated or approached decisional-cutoff
rules. Although Cronbach’s alpha estimate for the ACE was very good, caution
42
should be taken in interpreting the use of the ACE at this time; therefore it was
decided to exclude the use of the ACE in further analyses in the present study. With
respect to the patients’ appraisal of physicians’ communication effectiveness, all but
one of its items loaded strongly under its own component structure. However, one
item (physician’s ability to discuss treatment choices) loaded across two
components. When reliability analysis was conducted, the exclusion/inclusion of this
item did not affect the excellent internal reliability of 0.94. Additionally, the item has
good face validity and therefore was retained.
Treatment Choice Deliberation
Two measures were developed to comprise the treatment choice deliberation
process. These measures were designed to capture the level of patient participation
during treatment decision making regarding the extent to which various factors were
considered influential during treatment decision making (“factors influencing
treatment choice”) and regarding the extent to which patients self-reported the degree
of their own participation during treatment decision making (“involvement in
treatment decision making”). These two measures were expected to correlate
positively and to be indicative of patient participation in treatment choice
deliberation.
Factors influencing treatment choice. To measure the extent to which patient
factors had influence on patient treatment choices, a 13-item scale was designed to
capture this information and was evaluated at Time 1 (See Appendix D). Factors
43
thought important to prostate patients during their treatment decision making
comprised areas of physician recommendations (2 items), treatment issues (5 items),
and personal concerns or obligations (6 items). Patients were asked to rate the extent
to which each of these factors influenced their treatment decision on a 4-point scale
from 1 (not influential) to 4 (very influential). A summary score based on summing
the items from the 4-point scale would provide a poor indicator of the influential
nature of these factors; for instance, a summary score based on this method would
not differentiate between the extent to which factors were influential versus the
number of influential factors considered. Therefore responses were dichotomized
into “not considered influential” (rated as “0”) or “considered influential” (rated as
“1”) and summed in order to yield a summary score indicating the number of factors
influencing treatment choice. The possible score for items considered influential
ranges from 0-13; Cronbach’s alpha estimate for the 13-item scale was 0.80. Higher
scores indicate that more factors were considered during patients’ treatment decision
making.
Patient involvement in treatment decision making. To assess patients’ reports
of the extent of their involvement in the varying aspects of treatment choice
deliberation, an 8-item scale was designed and evaluated at Time 1 (See Appendix
E.1). These items comprised questions addressing the extent or degree to which
patients reported having control over their treatment decision making (1 item),
exercising treatment choice (3 items), participating in decision making (1 item),
44
deliberating over treatment options (1 item), and involving others in the treatment
decision making process (2 items). Items were answered on a 5-point scale ranging
from 1 (not at all) to 5 (extremely) and summed. Higher scores indicate greater
active involvement in one’s treatment decision making.
Principal components analyses resulted in a two-factor solution that
accounted for 75.7% of the variance and delineated clusters of items into
autonomous and collaborative choice deliberation domains (see Appendix E.2). All
but one of the variables, deliberating over choice, loaded on a solution in excess of
0.71, which is considered “excellent” by Comrey (1973). Cronbach’s alpha estimates
were excellent for the six-item “autonomous” treatment decision making sub-scale
and adequate for the two-item “collaborative” sub-scale. Deliberating over choice
did not violate any decisional cut-off rules, so this item was retained in the summary
scale. The Cronbach’s alpha for the 8-item scale was 0.90.
Coping. To measure the coping strategies of the patients, the Brief COPE
(Carver, 1997) was evaluated at Time 1. The Brief COPE was developed as a
revised, shortened measure of the original COPE measure of 60 items (Scheier,
Weintraub, & Carver, 1986), in part to reduce the time burden of participation and to
revise items thought to be more relevant to the cancer patient experience (Carver,
1997). The Brief COPE asks patients to respond to 28 statements about the ways in
which they dealt with the stress related to their prostate cancer on a 4-point scale,
ranging from 0 (I did not do this at all) to 3 (I did this a lot) and is organized in such
45
a way that two statements comprise one “subscale,” resulting in 14 subscales specific
to particular coping strategies of patients (e.g., problem solving coping, acceptance
coping, etc.). No total summary score of the COPE is computed, rather its authors
recommend evaluating the subscales based on investigator interest (Scheier et al.,
1986). Subscales of interest in this current study were concerned with those found to
be related to "problem-focused" coping as discussed by Folkman and Lazarus (1988)
and believed to be associated with the choice deliberation process (Petersen et al.,
2001; Truant & Bottorff, 1999). The subscales chosen for the study were (1) active
coping, (2) problem solving coping, (3) positive reframing coping, (4) emotional
support coping, and (5) instrumental support coping. These five “subscales”
comprised the 10-item “problem-focused coping” scale for the study. Scores are
summed with higher scores indicating greater use of problem-focused coping.
Internal consistency of the 10-item “problem-focused” coping subscale in the current
study was acceptable for analyses (= 0.72).
Treatment Choice Appraisal
Treatment decision making consonance. To measure whether patients
participated in the treatment decision making process to the extent that they wished,
two five-statement scales were developed to measure (1) patients’ actual treatment
decision making participation and (2) patients’ preference for treatment decision
making participation (see Appendix F). These scales were based on previous studies
that used forced-choice statements describing patient treatment decision making
46
participation (Gattellari et al., 2001; Degner & Sloan, 1992; Fallowfield, 2001). For
the purposes of the current study, the five statements in one forced-choice scale were
phrased to express patients’ actual role in treatment decision making (e.g., “I made
all the decisions about which treatment I will receive”) and the other five statements
on the other forced-choice scale were phrased to express patients' preferred role in
the present-perfect tense (e.g., "I should have made all the decisions about which
treatment I will receive."). The two scales are scored such that lower scores represent
patients having total control over their treatment decision making (e.g., score of 1 =
“I made all the decisions about which treatment I will receive”) and higher scores
represent physicians having total control over patient treatment decision making
(e.g., score of 5 = “My doctor made all the decisions about which treatment I will
receive”). Fallowfield (2001) suggested that the composite score of the actual and
preferred scales would stipulate a degree of consonance or dissonance between
preferred and actual role played, thus would be a measurable indicator of satisfaction
with one’s participation in the treatment decision making process. The possible
composite score ranges from -4 to +4, with 0 indicating complete consonance with
treatment decision making participation. Negative scores indicate patients’
preference to have been in more control than they were, whereas positive scores
indicate patients’ preference to have been in less control in treatment decision
making than they were. In order to evaluate consonance in bivariate analyses,
treatment decision making consonance was dummy coded to reflect any score of
47
dissonance (+/-4, +/-3, +/-2, +/-1) as a “1,” and consonance scores maintaining their
score of “0.” Consonance in treatment decision making was evaluated at Time 1.
Satisfaction with treatment decision. Two measures were developed to
comprise patient satisfaction with treatment choice and decision making
involvement. These two measures were expected to be moderately correlated, so as
to indicate that satisfaction with treatment choice was a meaningfully different
experience from that of being satisfied with one’s treatment decision making
involvement. Therefore, six questions were created to address these issues and
evaluated at Time 1 and Time 2.
4
Three items were worded to measure the extent to
which patients reported being satisfied with their treatment choice and three items
were worded to measure the extent to which patients reported being satisfied with
their involvement in the treatment decision making process. In order to maintain
thematic consistency, these 6 questions were combined with the eight-item scale
used to assess patient involvement in decision making (see Appendix E.1). All
measures relied on the same test instructions and response anchors; therefore
combining these into the same questionnaire was a practical choice. Questions were
answered on the same 5-point scale ranging from 1 (not at all) to 5 (extremely) and
summed into their respective summary scores. Higher scores indicate higher levels
of reported satisfaction.

4
Satisfied with treatment choice was the only measure in this section that was also evaluated at one-
month post-treatment as an outcome measure in accordance with Hypothesis 3.
48
Results of the principal components analysis can be review in Appendix E.3.
Principal components analysis was conducted on the six items to observe how well
these satisfaction items would relate to one another. What resulted was a two-factor
solution, accounting for 74.4% of the variance. Although the items trend in the
direction of the expected delineation of item clusters of satisfaction with treatment
choice and satisfaction with decision making involvement, notable overlap with two
variables between the two domains occurred (i.e., satisfied with choice and satisfied
with process). Under their expected domains, each of these two items’ factor scores
loaded a respective “excellent” and “very good” measure of that domain (per
Comrey, 1973), whereas their opposing loadings are considered “fair.” Given that
loadings did not violate decisional-cutoff rules, all items were retained, resulting in
two scales: One measuring three items of satisfaction with treatment choice and the
other measuring three items of satisfaction with treatment decision making
involvement. Cronbach’s alphas were very good for both scales, yielding a 0.80 for
satisfaction with treatment choice and 0.78 for satisfaction with treatment decision
making involvement.
Treatment-specific optimism. In order to measure patients’ optimism about
their treatment choice at pretreatment, a scale was developed to address specifically
this issue. The Treatment-Specific Optimism scale (TSO) was adapted from two
different studies measuring treatment expectancies for the consequence of breast
cancer treatment (Stanton et al., 1998) and outcome expectations from participation
49
in renal cell cancer clinical trials (Cohen et al., 2001). Specific to the POP study, the
TSO is a 7-item scale (see Appendix G.1) measuring the extent to which prostate
cancer patients anticipated that their treatment will cure their cancer (2 items),
increase their lifespan (1 items), work better than another treatment option (1 item),
result in additional treatment (1 item), and result in post-treatment morbidity (2
items, proposed as filler items). The questions are answered on a 5-point scale
ranging from 1 (not at all) to 5 (extremely) and summed. Higher scores indicate
higher degrees of optimism about one’s chosen treatment.
Principal components analysis on the original 7 items resulted in a three-
factor solution, accounting for 71.5% of the variance and delineated the two filler
items into their own domain. The two other components clustered variables based on
the similarity of content relating to issues of post-treatment morbidity and treatment
efficacy. Reliability statistics on the 7-item scale fell below the accepted coefficient
of 0.70 (= 0.65). Dropping the filler items, factor analysis continued to result in the
two-item factor solution (see Appendix G.2), yet Cronbach’s alpha continued to be
below accepted range (= 0.66). Further review revealed that one item: treatment
will work better than any other treatment available, may be addressing issues of
treatment comparison rather than treatment optimism; additionally, this item
meaningfully reduced the reliability coefficient, and was subsequently dropped from
the scale. The factor analysis of the remaining 4 items loaded into one factor,
50
accounting for 52.16% of the scale’s variance. The internal consistency of the scale
approaches adequacy at 0.69.
Optimism. Dispositional optimism was measured at pretreatment by the Life
Orientation Test, Revised (LOT-R; Scheier et al., 1994). The original LOT (Scheier
& Carver, 1985) was designed to assess dispositional optimism defined in terms of
generalized outcome expectancies, but its original items did not all focus as
explicitly on expectations for the future as theory dictated. The LOT-R was revised
in part to remedy this deficiency. It has established predictive and discriminant
validity, and has been used widely among chronically ill patients (Scheier et al.,
1994). Patients rate each of the 10 general expectation statements on a 5-point scale
ranging from 1 (strongly disagree) to 5 (strongly agree). Four statements are fillers
and not included in the summary score; thus 6 items (3 of which are reversed scored)
are summed into one score, with higher scores indicating a stronger tendency for one
to be dispositionally optimistic. Cronbach’s alpha in the present sample was 0.84.
Criterion Variables
Health-Related Quality of Life
General quality of life. General quality of life was assessed using the 3rd
version of the Functional Assessment of Cancer Therapy scale (FACT; Cella et al.,
1993) at both time points. The FACT was designed to measure multidimensional
aspects of quality of life in cancer patients. Four subscales of quality of life
(physical, functional, social, and emotional well-being) comprise a composite
51
FACT-G (general) score. Respondents answer questions concerning their quality of
life during the time since their prostate cancer diagnosis (Time 1) or since their
prostate cancer treatment (Time 2) on a 5-point scale ranging from 0 (not at all) to 4
(very much). Higher scores indicate better quality of life adjustment. The scale's
ability to discriminate patients on the basis of stage of disease and hospitalization
status supports its sensitivity and also demonstrates sensitivity to change over time.
Coefficients of reliability and validity have been reported as uniformly high (1993).
In the present study, Cronbach’s alpha for the FACT general score was 0.80 for
Time 1 and 0.64 for Time 2.
Prostate cancer quality of life. Prostate cancer quality of life was measured
at both time points using the prostate cancer specific subscale of the FACT; referred
to as the FACT-P (Esper et.al., 1997). This 12-item subscale measures patient
concerns related to prostate cancer symptomatology on the same scale described
above. Internal consistency of the FACT-P of the current sample at Time 1 was 0.69,
and at Time 2 was 0.75.
At one-month post-treatment, prostate cancer treatment concerns and
functioning were measured by the Prostate Cancer Index (PCI; Litwin et al., 1998).
This validated 20-item questionnaire assesses health concerns in three domains
(urinary, sexual and bowel) central to the quality of life of men with prostate cancer.
Items capture both the functioning aspect and the amount of “bother” each physical
domain has for patients over the past four weeks, and responses to items are
52
calculated in tertiary, quartile, or quinary percentages. An example of an item and its
response (with its assigned percentage coding) is:
1. Overall, how would you rate your ability to function
sexually?
1. Very poor (0)
2. Poor (25)
3. Fair (50)
4. Good (75)
5. Very good (100)
Higher scores in each of the three domain subscales indicate better adjustment. In the
present study, Cronbach’s alphas for the three domain scales at Time 2 were 0.90 for
urinary function, 0.90 for sexual function, and 0.81 for bowel function.
Psychosocial adjustment. Measures exemplifying psychosocial adjustment
for the purposes of the current study comprised assessment of (1) stress, (2) mood,
and (3) depression, and were evaluated at both time points.
Stress. The Impact of Events Scale-Revised (IES-R; Weiss & Marmar, 1997)
was measured at both time periods to capture patients’ subjective-experienced stress
as it is related to adverse life events. Patients respond to 22 items concerning their
prostate cancer distress, rating the degree to which bother or distress has affected
them during the past week on a 5-point scale from 0 (not at all) to 4 (extremely).
Items are summed and averaged to provide a total summary score resulting in higher
scores indicating greater degree of distress. Three subscales also can be computed to
examine distinctions of cognitive disruption of (1) intrusion, (2) avoidance, and (3)
hyperarousal as they relate to cancer-related stressful thoughts. In the present sample,
53
internal reliability for the summary score at both time points was excellent: Time 1
(=0.94) and Time 2 (=0.92).
Mood. Emotional states were assessed at both time points by the Positive and
Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988). The PANAS
comprises twenty mood descriptive words of which patients indicate how much they
have felt this way since their prostate cancer diagnosis (at Time 1) or during the past
month (at Time 2) on a 5-point scale, ranging from 1 (very slightly) to 5 (extremely).
Scores are summed into two subscales signifying positive and negative moods;
higher scores on each scale indicate greater positive or negative mood, respectively.
The PANAS has reported high internal consistency and the two subscales have been
demonstrated to be largely uncorrelated yet stable at appropriate levels over a 2-
month time period (1988). In the present study, internal reliability was good for the
two subscales at Time 1 (positive mood, =0.87 and negative mood, =0.89), and at
Time 2 (positive mood, =0.88 and negative mood, =0.80).
Depression. The Centers for Epidemiological Studies, Depression Scale
(CES-D; Radloff, 1977) was used to assess depression at both time points. The CES-
D was designed to measure depressive symptomatology in the general population. Its
20 items were selected from previously validated scales and cover the major
components of depressive symptomatology. Patients rate how much they have
experienced depressive feelings, attitudes, and behaviors on a 4-point scale, ranging
from 0 (rarely) to 3 (most of the time) over the past week. Items are summed into a
54
total summary score. Although the scale is not intended to diagnose clinical
depression, scores greater than or equal to 16 are suggestive of possible diagnosable
depression, requiring further assessment. The original coefficient alphas of the CES-
D were 0.85 in the general population and 0.90 in the depressed patient sample
(1977). Cronbach’s alphas in the present study demonstrated very good internal
reliability at Time 1 (= 0.91) and at Time 2 (=0.89).
Data Analysis
Data Inspection
Univariate statistics were used to describe the study sample and to inspect the
distribution of core variables. Missing data were replaced with individual mean
substitution for continuous data and sample mode values for categorical data. In
those cases where 10% or more of items were missing from a variable, that measure
was dropped from analyses (Pyle, 1999). Outliers and distribution skewness were
corrected with square root transformations; negative distributions were reflexed
5
prior to applying data transformations (Tabachnick & Fidell, 2001).
Heteroscedasticity was analyzed through evaluating Boxplots of core variables and
running Levene Statistic for homogeneity of variances; when necessary
heteroscedasticity was corrected with logarithmic transformation. Multicollinearity
was assessed at each regression equation by evaluating the variance inflation factor

5
A variable is “reflexed” by subtracting each value from the largest score and adding a value of 1.
Thus negative skewness is converted to one with positive skewness prior to transformation.
55
(VIF) (Pedhazur, 1997). After correcting for skewed data, no multicollinearity was
observed in the regressions.
Statistical Controls
In preliminary analyses, demographic and medical variables were correlated
with study core variables in order to determine whether they would confound the
relationship between predictor variables and criterion outcome variables. Examined
for this purpose were age, Gleason scores, PSA values, type of prostate cancer
treatment, income and education. In order to perform parametric statistics, income
and education were dummy coded based on the median split of their distributions.
Treatment modality was dummy coded based on whether a patient had that treatment
(score of “0”) or did not have that treatment (score of “1”), thus the four expectant
management patients were always scored as “1.” A correlations table of these
variables is provided (see Table 8) and a discussion of the findings is provided in the
regression results section below. Demographic and medical variables found to
correlate significantly with post-treatment health-related quality of life variables
were controlled for in subsequent regressions.
All regression analyses of post-treatment quality of life outcomes included
their respective pretreatment quality of life variable as a control variable in order to
assess for the unique effect that predictor variables had on change to patients’ quality
of life. Because the subscales of the Prostate Cancer Index (PCI) are designed as
post-treatment measures and thus not assessed at Time 1, the pretreatment FACT-P
56
served as a suitable pretreatment control variable used in PCI regressions because it
comprises prostate cancer symptomatology items similar to those addressed in the
PCI.
Main Analyses
Bivariate correlations were conducted to observe the relationships between
core variables, and multiple hierarchical regression equations were conducted to
evaluate the predictive nature of pretreatment measures upon post-treatment
outcomes. For hypotheses and questions related to the mediation of variables,
multiple regressions were conducted in order to show whether relationships between
hypothesized predictor and criterion variables existed, thus establishing that an effect
could be mediated. If this standard was met, the Baron and Kenny method for testing
mediation (Baron & Kenny, 1986) was conducted.
Power
Given the small sample size of the study, power to detect type I error is
limited. Analyses did reveal that correlation coefficients at or above 0.35 would have
adequate power (0.79) to detect significance at =0.05. Multiple regression
equations with 3 predictors for the sample size of 57 had power of 0.78 to detect
significance at =0.05 with a critical F of 2.78 or more, and effect sizes falling into
the medium range. Because five planned objectives were selected, alpha’s greater
than 0.01 (Miller, 1966) should be interpreted with caution.

57
Results
The results of this study are presented in four sections. The first section
describes the attrition and retention data of the non-participants and drop-outs,
respectively, and their applicable data were compared with study participants and are
discussed. The second section presents univariate and descriptive data on all
measures to describe the sample; moreover, data frequencies from available
subscales of the predictor variables also will be discussed in order to provide a more
thorough depiction of the sample concerning the treatment decision making process.
The third section presents data in order of the five specific objectives (the three
hypotheses and two questions) previously outlined. The last section comprises
exploratory analyses, conducted post-hoc, in order to examine further how age,
treatment, and disease severity interact with the treatment decision making process.
Attrition and Retention
Descriptive and comparative statistics contrasting participants from those
who refused participation and from those who returned their post-treatment
questionnaire after the cut-off date are provided. Table 1 lists the available data of
the study participants, drop-outs, and non-participants.
For patients who declined participation (“non-participants”), descriptive
statistics are reported for physician referral, chosen treatment (if indicated), and
responses on the refusal questionnaire. Because five of the seven items on the refusal
questionnaire were included in the pretreatment questionnaire, participant and non-
58
participant responses on these items were compared with independent t-tests. Chi-
square statistics were conducted to compare the frequencies of physician referral and
treatment choice between participants and non-participants.
Of the 44 men who declined participation, all men provided information
regarding which physician referred them, 50% provided information regarding their
choice of treatment, and 32% (14 men) agreed to answer the refusal questionnaire.
When compared to study participants, chi-square analyses failed to demonstrate
differences in the frequencies of physician referrals (S
2
=6.81, p=0.235). However,
differences in the frequencies of treatment choice were observed, indicating that
radiation patients were more likely to refuse to participate in the study (S
2
=10.09,
p=0.039) in comparison to other patients. Of the seven items of the refusal
questionnaire that corresponded to pretreatment questionnaire items addressing
involvement in treatment decision making and treatment-specific optimism,
independent t-tests failed to find any significant group differences between study
participants and non-participants.
For patients who returned their post-treatment questionnaire past the cut-off
date or not at all (“drop-outs”), descriptive statistics are reported for physician
referral, demographic and medical characteristics, pretreatment measures of the
treatment decision making process, and pretreatment measures of health-related
quality of life and psychosocial variables. Participants and drop-outs were compared

59
Table 1
Means and Frequencies of the Medical and Demographic Characteristics of the
Participants (n=57), Drop-Outs (n=15), and Non-Participants (n=22)*
POP Participants Drop-Outs
Non-
Participants
Variable Mean (Std) Mean (Std) Mean (Std)
Age 62.56 (7.55) 68.13 (8.56) N/A
Gleason 6.40 (0.78) 7.00 (0.88) N/A
PSA 7.41 (8.40) 10.54 (9.93) N/A

Variable n % n % n %
Prostatectomy
Yes 24 42% 6 40% 6 27%
No 33 58% 9 60% 16 73%
Radiation
Yes 18 32% 2 13% 9 41%
No 39 68% 13 87% 13 59%
Androgen Deprivation Therapy
Yes 11 19% 5 33% 4 18%
No 46 81% 10 67% 18 82%
Expectant Management
Yes 4 7% 0 0% 2 9%
No 53 93% 15 100% 20 91%
Combination Treatment
Yes 9 16% 2 13% 1 5%
No 48 84% 13 87% 21 95%
Clinic Referral
Surgical 23 40% 6 40% 15 34%
Radiation 12 21% 1 7% 15 34%
Hormone 16 28% 7 47% 12 27%
Self-Referred 6 11% 1 7% 2 5%
Education
High school or less 3 5% 2 13% N/A
Some college 9 16% 6 40%
College graduate 8 14% 2 13%
Postgraduate 37 65% 5 33%
Income
<60k 8 14% 5 33% N/A
60 - 100k 15 27% 5 33%
100 -140k 16 29% 2 13%
140 -180k 9 16% 0 0%
>180k 8 14% 3 20%
Note: * 50% of Non-Participants (22 of 44) provided treatment information, 100% provided
physician referral information, and none (0%) provided any other medical or
demographic information.
60
on non-parametric variables (i.e., education, income) using Chi-square statistics and
compared on parametric variables using independent t-tests. Alpha was set to 0.10 to
signify a conservative estimate of difference on all attrition and retention analyses.
Of the seventy-four patients who completed the pretreatment questionnaire, 2
withdrew participation at this time point. The remaining 72 men were sent the one-
month post-questionnaire, of whom only 57 returned this questionnaire prior to the
cut-off date. Retention analyses therefore included comparing data taken from the
pretreatment questionnaire of the 57 study participants with the 15 drop-outs. Chi-
square statistics demonstrated differences in the frequencies of education and income
between the two groups of men. Drop-outs had less education (S
2
= 4.87, p=0.027)
and trended toward having earned less income (S
2
= 3.12, p=0.078). There were no
differences in the frequencies of physician referrals between study participants and
drop-outs. Independent samples t-test of key variables comparing study participants
and drop-outs indicated that drop-outs were significantly older (t(70) = 2.51,
p=0.014), had higher Gleason scores (t(67) = 2.42, p=0.018), reported having less
positive affect (t(70) = -2.06, p=0.043), and less negative affect (t(70) = -1.90,
p=0.061) than study participants. There were no significant differences between
participants and drop-outs on variables of the treatment decision making process or
general and prostate cancer quality of life.
61
Descriptive Findings
Demographic and Medical Variables
Table 1 also lists the means and frequencies of the medical and demographic
characteristics of the patients. The mean age was 62.6 years and 95% of the men
reported being Caucasian. The reported medians for annual income and education
were a respective 100-120K and having achieved some post-baccalaureate education.
For their primary prostate cancer treatment, 24 men underwent prostatectomy, 18
had radiation treatment, 11 received androgen deprivation treatment (ADT), and 4
had chosen expectant management. Nine men received ADT neo-adjuvantly
6
with
surgery (n=1) and radiation (n=8). The intercorrelations of medical and demographic
variables were conducted and are listed in Table 2. Those with lower Gleason scores
were more likely to have had surgery, whereas those with higher Gleason scores
were more likely to have had radiation. High Gleason scores trended to relate with
older age (p=0.065) and with having had ADT (p=0.081). Given how treatment
modality appeared to be associated with some of the medical and demographic
variables, analyses of variance were conducted to observe whether any differences
between the three treatment groups of prostatectomy, radiation or hormone patients
were evident. Mean scores by treatment group are illustrated in Table 3 and results of

6
Neo-adjuvant treatment is often referred to as treatment that is given prior to primary treatment to
help make the next treatment step go more smoothly. For example, hormones can be given before
surgery in prostate cancer in order to shrink a large tumor so that it is easier to surgically remove.
62
Table 2
Intercorrelations between Demographic and Pretreatment Medical Variables†
Age Income
a
Education
b
Had Surgery
c
Had Radiation
c
Had ADT
c
Gleason PSA
Age 1
Income -0.202 1
Education 0.026 0.220 1
Had Surgery 0.207 0.095 0.043 1
Had Radiation -0.126 -0.174 -0.183 -0.579*** 1
Had ADT -0.154 -0.005 -0.155 -0.553*** 0.133 1
Gleason 0.246 -0.016 0.082 0.301* -0.360** -0.233 1
PSA 0.060 -0.160 0.094 -0.061 -0.181 -0.054 0.223 1
Notes:
†
For continuous variables, Pearson product-moment correlations were conducted. For dichotomous variables, Kendall's tau-b and
Spearman's rho correlations were conducted where applicable.
a
Income was coded as 0 = Less than 100K, 1 = More than 100K.
b
Education was coded as 0 = Bachelor's Education or less, 1 = Graduation Education.
c
Treatments for Surgery, Radiation or Hormones were coded as 0=Having had that procedure, 1=Not having had that procedure.
*p < .05, **p<.01, ***p<.001, two-tailed
63
the ANOVAs revealed no significant differences. A trend, however, was observed
for Gleason scores, such that radiation patients had higher Gleason scores in
comparison to surgery patients (p=0.053), as indicated by Scheffe post-hoc analysis.
Predictor Measures of the Treatment Decision Making Process
Results of descriptive analyses of the measures of the treatment decision
making process are listed in Table 4, and the intercorrelations of these measures can
be viewed in Table 5. In addition, Pearson’s and Spearman’s rho correlations were
conducted in order to observe how medical and demographic variables related to
measures of the treatment decision making process; these data are listed in Table 6.
Treatment information communication. With respect to the quantity of
treatment information communication, more than half the patients reported receiving
at least moderate amounts of treatment information from their medical team, as
measured by the CMT. With respect to patients’ reporting perceived understanding
about treatment information (measured by the PPU), no patient reported himself as
below average in understanding this information. Moreover, most men reported
having above average understanding, with 17.5% reported having “complete”
understanding of treatment information (a 7 on a 7-point scale). With respect to how
patients’ appraised the effectiveness of their physicians’ communication, 82.5% of
them rated their physicians’ communication as above average on the PACE scale,
with 32% of patients reporting that their physicians (or medical teams) were

64
Table 3
Summary of Analyses of Variance, Means, and Standard Deviations comparing
Treatment Groups on Medical and Demographic Variables

Treatment Group
Prostatectomy Radiation ADT* ANOVA
Variable (n = 24) (n = 18) (n = 11) F df
Age 1.413 (2,50)
M 60.63 64.17 64.09
(SD) (6.61) (6.88) (10.29)
Education** 0.581 (2,50)
M 7.50 8.22 7.82
(SD) (2.06) (2.05) (2.48)
Income** 0.483 (2,49)
M 6.21 6.83 6.00
(SD) (2.21) (2.43) (3.06)
Gleason Score 3.082 † (2,50)
M 6.21
a
6.78
b
6.36
ab

(SD) (0.66) (0.94) (0.51)
PSA Value 0.538 (2,50)
M 8.83 7.44 5.56
(SD) (12.47) (3.31) (2.08)

Notes:
Subscripts denoted for significant pairwise comparisons only. Significant differences do not share letters.
* ADT = Androgen Deprivation Therapy
** Both Education and Income were scored and computed as continuous variables; thus these data were
acceptable to be analyzed by ANOVA. A score of "7" on Education indicates having completed a college
education. A score of "6" on Income indicates earning at least $100K annually.
†
p< .10

65
Table 4
Means, Standard Deviations, and Ranges of Pretreatment Measures of the Treatment Decision Making Process
Pretreatment
Possible Range Actual Range
Mean STD Min - Max Min - Max
Treatment Information Communication
Communication with Medical Team 53.30 9.06 10 - 70 30 - 67
Patient Perceived Understanding 17.32 2.35 3 - 21 12 - 21
Patient Appraisal of Physician Communication Effectiveness 30.40 5.29 5 - 35 14 - 35
Treatment Choice Deliberation
Factors Influencing Treatment Choice 7.25 2.87 0 - 13 0 - 13
Involvement in Treatment Decision Making 35.40 5.03 8 - 40 8 - 40
Problem-Focused Coping 21.67 4.44 0 - 30 12 - 30
Treatment Choice Appraisal
Treatment Decision Making Consonance 0.16 0.68 -4 - +4 -3 - +2
Satisfaction with Treatment Choice 12.37 2.26 3 - 15 7 - 15
Satisfaction with Decision Making Involvement 13.54 2.12 3 - 15 6 - 15
Treatment-Specific Optimism 15.09 2.71 4 - 20 9 - 20
Dispositional Optimism 22.47 4.42 6 - 30 14 - 30
Note: Except for "Consonance with Decision Making," all measures are scored with higher scores indicating greater levels
of endorsement. Negative scores on the Consonance measure indicate preferences toward more autonomy, whereas
positive scores indicate preferences toward greater physician involvement.
66
Table 5
Intercorrelations between Measures of the Treatment Decision Making Process at Pretreatment†
Treatment Information Communication Treatment Choice Deliberation Treatment Choice Appraisal
CMT PPU PACE FITC ITDM
P-F
COPE TDMC SWC SWI TSO LOT
Treatment Information Communication
CMT 1
PPU 0.086 1
PACE 0.493*** 0.235 1
Treatment Decision Making
FITC -0.198 -0.159 -0.438** 1
ITDM 0.509*** -0.172 0.270* -0.165 1
P-F COPE 0.062 -0.102 -0.081 0.219 0.454*** 1
Treatment Choice Appraisal
TDMC -0.109 0.014 -0.347** 0.029 -0.036 -0.073 1
SWC 0.067 0.405** 0.416** -0.331* 0.182 -0.021 -0.102 1
SWI 0.495*** 0.155 0.463*** -0.380** 0.596*** 0.098 -0.314* 0.365** 1
TSO 0.056 0.130 0.153 0.004 0.092 0.057 -0.109 0.111 0.032 1
LOT 0.309* -0.135 -0.078 -0.094 0.317* 0.107 -0.129 0.036 0.210 -0.088 1
Notes:
CMT=Communication with Medical Team; PPU=Patient Perceived Understanding; PACE=Patient Appraisal of Physician Communication Effectiveness;
FITC= Factors Influencing Treatment Choice; ITDM=Involvement in Treatment Decision Making; P-F COPE=Problem-Focused Coping; TDMC=Treatment
Decision Making Consonance; SWC=Satisfaction with Choice; SWI=Satisfaction with Decision Making Involvement; TSO=Treatment-Specific Optimism;
LOT=Life Orientation Test (dispositional optimism).
†
For continuous variables, Pearson product-moment correlations were conducted. For the dichotomous variable, TDMC, Spearman's rho correlations were
conducted.
a
TDMC was coded as 0 = Consonant with treatment choice, 1 = Dissonant with treatment choice.
*p < .05, **p<.01, ***p<.001, two-tailed
67
Table 6
Correlations between Demographic/Medical Variables and Pretreatment Predictor Measures†
Age Income
a
Education
b
Gleason PSA
Had
Surgery
c
Had
Radiation
c
Had
ADT
c
Predictor Variables, Pretreatment
Treatment Information Communication
Communication with Medical Team -0.021 0.380** 0.172 -0.057 -0.354** -0.083 -0.181 -0.074
Patient Perceived Understanding 0.139 -0.010 0.039 -0.031 -0.257 0.055 -0.164 0.120
PACE 0.320* 0.102 -0.143 0.136 -0.232 -0.086 -0.084 0.010
Treatment Choice Deliberation
Factors Influencing Treatment Choice -0.272* -0.167 -0.039 -0.325* -0.019 -0.074 0.227 0.078
Involvement in Treatment Decision Making 0.007 0.117 -0.089 -0.026 -0.110 -0.210 0.050 -0.055
Problem-Focused Coping -0.309* 0.078 -0.158 -0.199 0.132 -0.309* 0.247 0.158
Treatment Choice Appraisal
Treatment Decision Making Consonance 0.023 -0.235 0.049 0.144 0.126 -0.129 0.189 0.049
Satisfaction with Choice 0.163 0.075 -0.140 0.059 -0.168 -0.054 0.067 0.173
Satisfaction with Decision Making Involvement 0.098 0.167 -0.118 -0.104 -0.243 -0.186 0.075 0.084
Treatment-Specific Optimism -0.022 -0.010 -0.005 -0.224 0.009 -0.459** -0.076 0.367**
LOT 0.099 0.190 0.137 0.026 0.203 -0.012 -0.033 -0.035
Notes: PACE = Patient Appraisal of Physician Communication Effectiveness; LOT = Life Orientation Test (dispositional optimism).
a
Income was coded as 0 = Less than 100K, 1 = More than 100K.
b
Education was coded as 0 = Bachelor's Education or less, 1 = Graduation Education.
c
Treatments for Surgery, Radiation or Hormones were coded as 0 = Having had that procedure, 1 = Not having had that procedure.
†
For continuous variables, Pearson product-moment correlations were conducted. For dichotomous variables, Kendall's tau-b or Spearman's
rho correlations were conducted where applicable.
*p < .05, **p<.01, two-tailed
68
“excellent” in communicating to them about treatment information (a 7 on a 7-point
scale).
As indicated in Table 5, patient perceived understanding (PPU) was not
associated with the quantity of information (CMT) or how effectively it was
communicated by physicians (PACE). However, a relationship was observed
between the CMT and the PACE scales; greater communication with medical team
was associated with better appraisal of physician communication effectiveness
(p<0.001). Higher scores on communication with medical team and appraisal of
physician communication also can be observed with respect to demographic
variables as indicated in Table 6. Having received greater communication with
medical team was associated with greater income (p<0.01) and older age was related
to higher appraisal of physician communication (p<0.05). Higher PSA was related to
having received less communication with medical team (p<0.01) and trended to
associate with lower levels of patient perceived understanding (p=0.054).
Treatment choice deliberation. In general, the men in the POP study
indicated that they were active in the treatment choice deliberation process. Patients
on average reported that at least 7 of the 13 listed factors were influential in their
treatment decision making. On average, men reported that their physician’s opinion
had “moderate” influence on their treatment choice (a 3 on a 4-point scale), with
more than one-third reporting their physician’s opinion to have been “very”
influential (a 4 on a 4-point scale). On average, the men reported that treatment
69
concerns were somewhat influential, followed by personal factors having little
influence on their treatment choices. In terms of patient involvement in decision
making, on average participants reported that they were very involved, with 54% of
the men indicating they were involved in treatment decision making to a great extent.
In terms of collaborative treatment decision making, most men indicated that they
involved others to a “moderate” extent (a 3 on a 5-point scale), with 28% of the men
reporting collaboration to a “great” extent (a 5 on a 5-point scale). With respect to
coping, the ways in which the patients coped with their prostate cancer varied. In
general, the men reported using problem-focused coping “a medium amount of the
time” (a 2 on a 4-point scale). However, review of the specific components of
problem-focused coping showed that more than half the patients reported using
problem solving, active strategies, and instrumental support to cope with their cancer
“a lot of the time” (a 3 on a 4-point scale), where only approximately one-third of the
men reported using emotional support and positive reframing coping strategies “a lot
of the time.”
As indicated in Table 5, the influence of factors in decision making was not
associated with patients’ report of their involvement in decision making or their
coping strategies; however as expected, problem-focused coping and decision
making involvement were significantly related (p<0.001), indicating that the more
involved one was in decision making, the more he engaged in problem-focused
coping. As indicated in Table 6, treatment decision making did not relate to any
70
medical or demographic factor. However with respect to factors influencing
treatment choice, age and Gleason scores were related such that, younger age
(p<0.05) and lower Gleason scores (p <0.05) were associated with a greater number
of factors considered influential during decision making. Greater use of problem-
focused coping strategies was related to younger age and to having had
prostatectomy (p’s <0.05).
Treatment choice appraisal. With respect to treatment decision making
consonance, 77% of patients’ composite scores indicated that they participated in the
treatment decision making process to the extent that they wished. Eleven patients
indicated that they had more control over decision making than they would have
preferred, whereas 2 patients indicated that their physician had more control in the
process than these patients would have preferred. Eighty percent of patients reported
that they made the final decision about their treatment, whereas 67% of the men
reported preferring to have made the final decision and 21% reported preferring that
they and their doctor should have made the final decision about their treatment
together.
Most patients reported satisfaction with their treatment choice and their
decision making involvement. Sixty-eight percent of the men reported being “very-
to-extremely” satisfied with their treatment choice (scores of 4 or more on a 5-point
scale), whereas 88% of the men reported this level of satisfaction with their decision
making involvement. As indicated in Table 5, a significant correlation was observed
71
between satisfaction with treatment choice and satisfaction with treatment decision
making involvement (p<0.01), indicating that higher levels of treatment choice
satisfaction were associated with higher levels of satisfaction with treatment decision
making involvement. With respect to treatment decision making consonance,
consonance in treatment decision making was associated with satisfaction with
treatment decision making involvement (p<0.05), indicating that being consonant
was related to more satisfaction. However, contrary to expectations, consonance was
not found to correlate with treatment choice satisfaction (p=0.423).
With respect to the men’s positive expectations about their chosen treatment,
on average they were very optimistic about their treatment, with nearly half of the
men reporting a mean score of being very optimistic or higher. With respect to
dispositional optimism, as measured by the LOT, nearly half the participants
“agreed” or “strongly agreed” (scores of 4 and 5 on a 5-point scale) with statements
describing themselves as having an optimistic disposition. This finding is consistent
with others studies assessing dispositional optimism in prostate cancer patients
(Bjorck et al., 1999. Treatment-specific optimism did not correlate with dispositional
optimism (p=0.513). With respect to medical characteristics, prostate cancer
treatments were related to treatment-specific optimism (see Table 6). Having greater
treatment-specific optimism was related to having had prostatectomy, yet having less
treatment-specific optimism was related to having had hormone therapy (p’s<0.001).

72
Criterion Measures of Health-Related Quality of Life
Descriptive statistics and paired t-tests for pre- and post-treatment measures
of the FACT and psychosocial adjustment scales are listed in Table 7. Pearson’s and
Spearman’s rho correlations were conducted in order to observe the associations of
medical and demographic variables and measures of quality of life (see Table 8).
General quality of life. With the exception of emotional well-being, findings
revealed that the prostate cancer patients in this sample averaged at or above the
gender-matched norms for cancer patients and healthy men on domains of health-
related quality of life from the FACT validation study (Brucker, Yost, Cashy,
Webster, & Cella , 2005). Figure 2 lists these data. On average, the participants in
the study reported having good general quality of life at pretreatment and at one-
month post-treatment. In examining change over time (see Table 7), men reported a
decline in physical well-being at one-month post-treatment in comparison to their
pretreatment status (p<0.001) and reported improvement in emotional well-being at
one-month after primary treatment in comparison to their pretreatment reporting
(p<0.001). As indicated in Table 8, age was correlated with two quality of life
variables, indicating that older age was associated with better emotional well being
(p=0.007), as well as better general health-related quality of life (p=0.042).

73
Figure 2
Means for the Functional Assessment of Cancer Therapy (FACT) between POP
Study Patients, FACT General Population, and FACT Cancer Patients*

Means of Pre-Treatment and Post-Treatment FACT General
Summary Scores
50
53
56
59
62
65
68
71
74
77
80
83
86
89
92
95
98
Pre Tx General WB Post Tx General WB
POP patients
FACT Norms
FACT Cancer
Means of Pre-Treatment FACT Subscales
0
3
6
9
12
15
18
21
24
27
30
33
36
39
Emotional WB Functional WB Physical WB Social WB
POP patients
FACT Norms
FACT Cancer
Means of Post-Treatment FACT Subscales
0
3
6
9
12
15
18
21
24
27
30
33
36
39
Emotional WB Functional WB Physical WB Social WB
POP patients
FACT Norms
FACT Cancer
* Brucker et al., 2005
74
Table 7
Comparisons of Means and Standard Deviations of Pre- and One-Month Post-
Treatment Health-Related Quality of Life Measures

Pretreatment Post-Treatment Paired-Samples
Measures Mean STD Mean STD t Statistic p value
General Quality of Life
FACT-G Summary
General Well Being 86.14 13.87 85.07 12.74 0.907 0.368

FACT Subscales
Emotional Well Being 17.88 4.26 20.00 3.51 -4.871 <0.001
Functional Well Being 21.44 4.56 20.79 4.22 1.141 0.259
Physical Well Being 25.19 3.81 23.35 4.59 3.462 0.001
Social Well Being 21.63 5.62 20.93 4.13 1.326 0.190

Prostate Cancer Quality of Life
FACT-P
Prostate Cancer Well Being 38.37 5.78 34.91 6.83 4.180 0.001

Psychosocial Adjustment
Stress: IES-R 2.17 1.85 1.84 1.73 1.067 0.291
Mood: PANAS
Positive Mood 32.18 6.97 33.39 7.07 -0.960 0.341
Negative Mood 19.58 6.96 14.18 5.08 5.003 <0.001
Depression: CES-D 10.18 8.68 9.75 8.16 0.336 0.738
Satisfaction: Satisfied with
Treatment Choice* 12.37 2.26 12.70 2.08 -1.256 0.214

Notes:
FACT=Functional Assessment of Cancer Therapy; FACT-G=Functional Assessment of Cancer Therapy,
General; FACT-P=Functional Assessment of Cancer Therapy, Prostate Specific; PCI=Prostate Cancer
Index; IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for
Epidemiological Studies, Depression Scale.
* Although Satisfaction with Treatment Choice is conceived as part of the treatment decision making
process, this measure also served as an outcome measure for Hypothesis 2, therefore its inclusion here.

75
Table 8
Correlations between Demographic/Medical Variables and One-Month Post-Treatment Dependent Criterion Measures†
Age Income
a
Education
b
Gleason PSA Had Surgery
c
Had Radiation
c
Had ADT
c
Criterion Variables, One-Month Post Treatment
General Quality of Life
FACT-G Summary: General Well Being 0.270* 0.003 -0.077 0.033 -0.073 -0.056 0.104 0.001
FACT Subscales: Emotional Well Being 0.352** -0.133 -0.036 -0.008 -0.110 -0.125 0.059 0.122
Functional Well Being 0.200 0.166 0.029 0.119 -0.084 0.044 -0.033 -0.006
Physical Well Being 0.149 0.004 -0.010 0.003 -0.195 0.125 0.122 -0.034
Social Well Being 0.192 -0.028 -0.181 0.022 0.146 -0.180 0.109 -0.037
Prostate Cancer Quality of Life
FACT-P: Prostate Cancer Well Being -0.030 0.069 0.040 0.143 0.148 0.274* -0.134 -0.115
PCI: Bowel Functioning -0.087 0.087 0.019 -0.103 0.083 0.118 0.073 -0.192
Sexual Functioning -0.373** -0.020 -0.094 -0.166 0.019 0.082 0.176 0.127
Urinary Functioning 0.065 0.020 0.023 0.205 0.090 0.512*** -0.301* -0.385**
Psychosocial Adjustment
Stress: IES-R -0.082 -0.165 0.159 -0.193 0.039 -0.030 0.071 -0.098
Mood: PANAS: Positive Mood -0.026 0.024 0.003 0.002 0.049 0.014 0.006 -0.092
Negative Mood -0.159 -0.068 0.015 -0.065 -0.016 -0.037 0.022 -0.021
Depression: CES-D -0.005 0.004 0.075 -0.033 -0.037 -0.113 0.094 0.020
Notes:
FACT=Functional Assessment of Cancer Therapy; FACT-G=Functional Assessment of Cancer Therapy, General; FACT-P=Functional Assessment of Cancer
Therapy, Prostate Specific; PCI=Prostate Cancer Index; IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for
Epidemiological Studies Depression Scale.
a
Income was coded as 0 = Less than 100K, 1 = More than 100K.
b
Education was coded as 0 = Bachelor's Education or less, 1 = Graduation Education.
c
Treatments for Surgery, Radiation or Hormones were coded as 0 = Having had that procedure, 1 = Not having had that procedure.
†
For continuous variables, Pearson product-moment correlations were conducted. For dichotomous variables, Spearman's rho correlations were conducted where
applicable. *p < .05, **p<.01, ***p<.001, two-tailed
76
Prostate cancer quality of life. On average, patients reported having good
prostate cancer quality of life as measured by the FACT-P at both time periods.
Although the men reported having little trouble in this domain at both time points, a
significant decrease in prostate-cancer quality of life score is observed at one month
post-treatment (p<0.001), these data are listed in Table 7.
Results of the means and frequencies from the Prostate Cancer Index (PCI)
are listed in Table 9 and indicate that most men in the study were above the mid-
point on scales measuring bowel and urinary function at one-month post-treatment.
Correspondingly, 35% of men reported not being bothered by their urinary
functioning and 54% reported not being bothered by their bowel functioning one
month post-treatment. With respect to sexual functioning, however, the descriptive
findings indicated that most patients were below the mid-point on the scale
measuring sexual function and 53% reported being bothered by this. As indicated in
Table 8, older age was associated with poorer sexual functioning (p<0.01). Having
had prostatectomy was associated with poorer prostate cancer well being (p<0.05)
and poorer urinary functioning (p<0.001), and not having had radiation or hormone
therapy was associated with better urinary functioning (p’s<0.05).
Psychosocial adjustment. The results of measures of psychosocial adjustment
for both time periods are listed in Table 7. In general, patients reported having good
psychosocial adjustment at pre- and post-treatment. However, twenty-six percent of
77
Table 9
Frequencies of Bother, and Means and Standard Deviations of the Prostate Cancer Index (PCI) at one-Month Post-
Treatment (N=57)
Frequencies of Bother Descriptive Statistics
No Very Small Small Moderate Big Possible Actual
Measure Problem Problem Problem Problem Problem Mean STD Range Range
Prostate Cancer Index
Bowel Function 54.4% 19.3% 12.3% 14.0% 0.0% 79.62 19.35 0-100 20-100
Sexual Function 10.5% 12.3% 10.5% 14.0% 52.6% 31.37 25.44 0-100 0-81
Urinary Function 35.1% 29.8% 15.8% 12.3% 7.0% 76.93 27.99 0-100 7-100
78
78
the men scored above the cut-off score for an elevated probability of having
diagnosable depression as measured by the CES-D at pretreatment and 12% of the
men scored in this manner after one month post-treatment. The mean depression
scores of the study sample are consistent with another study assessing depression in
prostate cancer patients (Blank & Bellizzi, 2006). PANAS scores revealed that the
participants also reported having moderately high positive mood and very little
negative mood at both time points. Although men reported having little negative
mood in general, paired-samples t-test indicated further improvement in negative
mood after one-month post-treatment (p<0.001). With respect to experiencing stress,
results of the IES-R summary scale indicated that patients experienced “a little bit”
of stress at both time points (a 1 on a 4-point scale). This finding of low stress is
consistent with another study assessing stress in prostate cancer patients (Joseph et
al., 2006). As indicated in Table 8, no demographic or medical variable was
associated with one-month post-treatment psychosocial adjustment measure.
Predictive Findings
Covariates
In order to account for any potential confounds in examining post-treatment
quality of life measures, preliminary analyses were performed to assess the relations
of demographic and medical variables to post-treatment measures of quality of life.
Therefore, partial correlations were performed in order to observe whether age,
treatment modality (i.e., surgery, radiation, and ADT), Gleason score, and PSA
79
79
values were related to criterion measures after controlling for each of the five
covariates in turn. Table 10 lists these results; for comparative purposes, the reader
may wish to refer to the zero-order correlations of these variables in Table 8.
Results revealed that, in general, age maintained its relationship with
criterion measures after controls were entered, and thus was chosen to serve as a
control variable in those regression analyses predicting general well-being,
emotional well-being, and sexual functioning. Neither Gleason scores nor PSA
values gained any relationships with criterion measures and therefore were not
included as covariates in any of the regression analyses. After controlling for age and
medical variables, both surgery and radiation treatments maintained, gained, or lost
significant associations with some of the criterion measures. Having had surgery had
the strongest significant association with sexual and urinary functioning and thus was
selected to serve as a covariate in regression equations predicting these outcomes.
Question 1 – Treatment Communication Information
Exploratory bivariate correlational analyses were conducted in order to
observe how treatment information communication measures would relate to
pretreatment and post-treatment health-related quality of life. Results of the
correlations can be found in Table 11 and indicated that the three measures
comprising “treatment information communication” (i.e., medical team
communication, patient perceived understanding, and appraisal of physician
communication) related to few health-related quality of life variables at either time
80
Table 10
Partial Correlations of Demographic/Medical Co-Variates and One-Month Post-Treatment Criterion Variables (N=57)
a
Age Gleason PSA Had Surgery
b
Had Radiation
b
Had ADT
b
Criterion Variables, One-Month Post Treatment
General Quality of Life
FACT-G Summary: General Well Being 0.296* 0.033 -0.121 -0.079 0.036 0.037
FACT Subscales: Emotional Well Being 0.420** -0.015 -0.191 0.180 -0.058 0.100
Functional Well Being 0.190 0.111 -0.126 0.011 0.061 0.062
Physical Well Being 0.150 0.093 -0.204 0.204 0.254 0.172
Social Well Being 0.236 -0.088 0.124 -0.231 -0.084 -0.161
Prostate Cancer Quality of Life
FACT-P: Prostate Cancer Well Being -0.125 0.085 0.179 0.249 0.047 0.077
Prostate Cancer Index (PCI): Bowel Functioning -0.112 -0.107 0.179 0.113 0.119 -0.158
Sexual Functioning -0.435*** -0.009 0.125 0.462*** 0.353** 0.300*
Urinary Functioning -0.103 0.060 0.199 0.394*** 0.027 -0.103
Psychosocial Adjustment
Stress: IES-R 0.050 0.203 -0.016 0.038 0.112 0.052
Mood: PANAS: Positive Mood -0.041 0.007 0.056 -0.020 -0.004 -0.092
Negative Mood 0.149 0.050 -0.034 0.003 0.061 0.013
Depression: CES-D 0.018 0.008 -0.047 0.043 0.051 0.004
Note:
FACT=Functional Assessment of Cancer Therapy; FACT-G=General Summary Scale; FACT-P=Prostate Specific Scale; PCI=Prostate Cancer Index.
IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression Scale.
a
Partial correlations were computed for each variable and controlling for the remaining 5 variables in turn.
b
Treatments for Surgery, Radiation or Hormones were coded as 0 = Having had that procedure, 1 = Not having had that procedure.
*p < .05, **p<.01, ***p<.001, two-tailed
81
point. Of the associations observed, no consistent pattern of relationships was evident.
However, patients’ appraisal of physician communication effectiveness was
significantly related to general quality of life at both time points, indicating that higher
appraisal of physicians’ communication was associated with better general well-being at
both time points (p=0.016 and p=0.009, respectively).
Exploratory hierarchical regression equations were conducted to observe how
the measures of treatment information communication would account for post-treatment
quality of life, after controlling for respective pretreatment variables of quality of life
and selected age and surgery covariates. Due to the number of one-month post-
treatment measures, regression tables are separated by three aspects of quality of life
such as (1) general quality of life, (2) prostate cancer quality of life, and (3)
psychosocial adjustment. These results are presented in Tables 12a-c, respectively.
Partial correlations, rather than beta weights, are reported in order to provide a measure
of each predictor as it related to criterion measures after all other predictors had been
controlled for
7
. Results indicated that for all the FACT scales, which include the FACT-
G summary scale, its four FACT subscales, and the FACT-P summary scale, the
respective pretreatment FACT measures performed as the strongest contributors to post-
treatment FACT measures (see Table 12a and the first equation in Table 12b) of quality
of life. As for the three post-treatment prostate

7
Standardized beta weights represent slopes that are functions of zero-order correlations and often
regarded as analogous to semi-partial correlations. Given the importance to observe for change scores in
these predictive models, reporting partial correlations allows the reader to observe the relation of each
predictor as relates to criterion measures, after controlling for all other predictors (Thordike and Dinnel,
2000).
82
Table 11
Correlations of Measures of Pretreatment Information Communication and Pre-
and One-Month Post-Treatment Health-Related Quality of Life (n=57)
Treatment Information Communication
Pretreatment Health-Related Quality of Life CMT PPU PACE
General Quality of Life
FACT-G Summary: General Well Being 0.145 0.096 0.318*
FACT Subscales: Emotional Well Being 0.132 0.121 0.246
Functional Well Being 0.127 0.128 0.225
Physical Well Being 0.008 -0.156 0.031
Social Well Being 0.146 0.128 0.363**
Prostate Cancer Quality of Life
FACT-P: Prostate Cancer Well Being 0.164 0.028 0.017
Psychosocial Adjustment
Stress: IES-R -0.125 0.030 -0.090
Mood: PANAS: Positive Mood 0.069 0.263* -0.008
Negative Mood -0.222 -0.001 -0.099
Depression: CES-D -0.305* -0.023 -0.123
Post-Treatment Health-Related Quality of Life
General Quality of Life
FACT-G Summary: General Well Being 0.298* 0.158 0.342**
FACT Subscales: Emotional Well Being 0.255 0.146 0.320*
Functional Well Being 0.308** 0.109 0.239
Physical Well Being 0.121 0.178 0.230
Social Well Being 0.127 0.045 0.209
Prostate Cancer Quality of Life
FACT-P: Prostate Cancer Well Being 0.097 -0.078 0.127
PCI: Bowel Functioning -0.019 -0.102 -0.071
Sexual Functioning -0.169 -0.109 0.404**
Urinary Functioning -0.156 0.008 0.010
Psychosocial Adjustment
Stress: IES-R -0.133 -0.053 -0.050
Mood: PANAS: Positive Mood 0.261* 0.042 0.049
Negative Mood -0.127 -0.053 -0.025
Depression: CES-D -0.230 -0.108 0.004
Notes:
CMT=Communication with Medical Team; PPU=Patient Perceived Understanding; PACE=Patient
Appraisal of Physician Communication Effectiveness; FACT=Functional Assessment of Cancer Therapy;
FACT-G= Functional Assessment of Cancer Therapy, General; FACT-P=Functional Assessment of
Cancer Therapy, Prostate Specific; PCI=Prostate Cancer Index; IES-R=Impact of Events Scale;
PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression
Scale.
*p < .05, **p<.01, two-tailed
83
83
Table 12a
Summary of Hierarchical Regression Analyses for Pretreatment Information Communication, controlling for
Pretreatment Quality of Life, Predicting FACT-G Summary and Subscale
Measures of One-Month Post-Treatment Quality of Life (N=56)
Adj F YF Partial
Measures df R2 Value YR
2
Value r
a
FACT - General Well Being, Post-Treatment
Step 1 (2,54) 0.498 28.74 ***
FACT General, Pretreatment 0.678 ***
Age 0.083
Step 2 (5,51) 0.517 13.00 *** 0.045 1.730
Communication with Medical Team 0.246
Patient Perceived Understanding 0.101
PACE -0.002
FACT - Emotional Well Being, Post-Treatment
Step 1 (2,54) 0.314 13.85 ***
FACT Emotional, Pretreatment 0.473 ***
Age 0.129
Step 2 (5,51) 0.322 6.32 *** 0.044 1.200
Communication with Medical Team 0.161
Patient Perceived Understanding -0.049
PACE 0.081
FACT - Functional Well Being, Post-Treatment
Step 1 (1,55) 0.263 20.97 ***
FACT Functional, Pretreatment 0.516 ***
Step 2 (4,52) 0.329 7.88 *** 0.101 2.818 *
Communication with Medical Team 0.348 **
Patient Perceived Understanding 0.034
PACE -0.046
84
84
Table 12a (continued)
Summary of Hierarchical Regression Analyses for Pretreatment Information Communication, controlling for
Pretreatment Quality of Life, Predicting FACT-G Summary and Subscale
Measures of One-Month Post-Treatment Quality of Life (N=56)
Adj F YF Partial
Measures df R2 Value YR
2
Value r
a
FACT - Physical Well Being, Post-Treatment
Step 1 (1,55) 0.058 4.45 ***
FACT Physical, Pretreatment 0.304 *
Step 2 (4,52) 0.090 2.39 *** 0.080 1.646
Communication with Medical Team 0.025
Patient Perceived Understanding 0.188
PACE 0.149
FACT - Social Well Being, Post-Treatment
Step 1 (1,55) 0.497 56.42 ***
FACT Social, Pretreatment 0.701 ***
Step 2 (4,52) 0.476 13.72 *** 0.007 0.252
Communication with Medical Team 0.077
Patient Perceived Understanding -0.049
PACE -0.093
Notes:
FACT = Functional Assessment of Cancer Therapy; PACE = Patient Appraisal of Physician Communication Effectiveness.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
*p < .05. **p < .01. ***p < .001.
85
85
Table 12b
Summary of Hierarchical Regression Analyses for Pretreatment Information Communication, controlling for
Pretreatment Prostate Cancer Quality of Life, Predicting FACT-P and PCI Measures of One-Month Post-
Treatment Prostate Cancer Quality of Life (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
FACT - Prostate Cancer Well Being, Post-Treatment
Step 1 (1,55) 0.258 20.46 ***
FACT-P, Pretreatment 0.539 ***
Step 2 (4,52) 0.258 5.87 ** 0.040 1.007
Communication with Medical Team -0.085
Patient Perceived Understanding -0.157
PACE 0.207
PCI - Bowel Functioning, Post Treatment
Step 1 (1,55) 0.010 0.44
FACT-P, Pretreatment 0.104
Step 2 (4,52) 0.058 0.23 0.010 0.170
Communication with Medical Team -0.071
Patient Perceived Understanding -0.050
PACE 0.088
PCI - Sexual Functioning, Post Treatment
Step 1 (3,53) 0.192 5.44 **
FACT-P, Pretreatment 0.268
Age -0.328 *
Prostatectomy
b
0.179
Step 2 (6,50) 0.228 3.76 ** 0.075 1.821
Communication with Medical Team -0.079
Patient Perceived Understanding -0.023
PACE -0.215
86
86
Table 12b (continued)
Summary of Hierarchical Regression Analyses for Pretreatment Information Communication, controlling for
Pretreatment Prostate Cancer Quality of Life, Predicting FACT-P and PCI Measures of One-Month Post-
Treatment Prostate Cancer Quality of Life (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
PCI - Urinary Functioning, Post Treatment
Step 1 (2,54) 0.291 12.49 ***
FACT-P, Pretreatment 0.203
Prostatectomy
b
0.561 ***
Step 2 (5,51) 0.301 5.82 *** 0.047 1.253
Communication with Medical Team -0.235
Patient Perceived Understanding -0.087
PACE 0.212
Notes: FACT-P = Functional Assessment of Cancer Therapy, Prostate Cancer Specific; PCI = Prostate Cancer Index; PACE = Patient Appraisal of
Physician Communication Effectiveness.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
b
Prostatectomy was coded as 0 = Having had prostatectomy, 1 = Not having had prostatectomy.
*p < .05. **p < .01. ***p < .001.
87
87
Table 12c
Summary of Hierarchical Regression Analyses for Pretreatment Information Communication, controlling for
Pretreatment Psychosocial Adjustment, Predicting One-Month Post-
Treatment Psychosocial Adjustment (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
IES-R: Stress, Post Treatment
Step 1 (1,55) 0.004 1.23
IES-R, Pretreatment 0.138
Step 2 (4,52) 0.013 0.82 0.037 0.685
Communication with Medical Team -0.173
Patient Perceived Understanding -0.074
PACE 0.152
PANAS-P: Positive Mood, Post Treatment
Step 1 (1,55) 0.012 0.36
PANAS-P, Pretreatment 0.053
Step 2 (4,52) 0.010 1.14 0.074 1.401
Communication with Medical Team 0.269 *
Patient Perceived Understanding 0.027
PACE -0.097
PANAS-N: Negative Mood, Post Treatment
Step 1 (1,55) 0.004 0.80
PANAS-N, Pretreatment 0.095
Step 2 (4,52) 0.034 0.54 0.025 0.454
Communication with Medical Team -0.142
Patient Perceived Understanding -0.069
PACE 0.114
88
88
Table 12c (continued)
Summary of Hierarchical Regression Analyses for Pretreatment Information Communication, controlling for
Pretreatment Psychosocial Adjustment, Predicting One-Month Post-
Treatment Psychosocial Adjustment (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
CES-D: Depression, Post Treatment
Step 1 (1,55) 0.060 4.59 *
CES-D, Pretreatment 0.230
Step 2 (4,52) 0.049 1.73 0.040 0.789
Communication with Medical Team -0.171
Patient Perceived Understanding -0.125
PACE 0.134
Notes: IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression
Scale; PACE=Patient Appraisal of Physician Communication Effectiveness.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
*p < .05. **p < .01. ***p < .001.
89
cancer well-being variables of bowel, sexual, and urinary functioning (as measured
by the PCI), pretreatment FACT-P did not account for the variance in these one-
month outcomes (see Table 12b). The pretreatment measures of psychosocial
adjustment also did not account for the variance in each of their respective one-
month outcomes, with the exception of depression (see Table 12c). With regard to
how well measures of treatment information communication predicted post-
treatment health-related quality of life, the data revealed that pretreatment
information communication did not contribute to any one-month outcome variable,
with the exception of functional well-being. Treatment information communication
accounted for an additional 10% of the variance in post-treatment functional well
being, revealing that greater communication with one’s medical team was associated
with better functional well-being (Z=0.16, SE=0.06, t = 2.67, p=0.01).
Hypothesis 1 – Treatment Choice Deliberation
Table 13 lists the bivariate correlations of the measures of pretreatment
choice deliberation and post-treatment health-related quality of life. Results indicated
that factors influential to treatment choice, treatment decision making involvement,
and problem-focused coping related to few post-treatment health-related quality of
life variables. A pattern was evident, however, with respect to patient involvement in
treatment decision making significantly correlating with all but one subscale on the
FACT. These findings indicated that the more involved patients were in their
90
decision making process, the better their quality of life at one-month post-treatment
(p’s<0.05).
Hierarchical regression analyses were conducted to test the hypothesis that
pretreatment measures of the “treatment choice deliberation process” (i.e., factors
influencing choice and involvement in decision making) would predict health-related
quality of life at one-month post-treatment above and beyond that of pretreatment
health-related quality of life, treatment information communication, and problem-
focused coping. Prior to conducting the regressions, the three measures comprising
treatment information communication were summarized into one measure to serve as
a single control variable in these analyses; this decision was made due to the
restrictions of a small sample size limiting the number of variables allowed in
regression analyses. Furthermore, the measures were answered in the same scale
using the same Likert-type anchors, and yielded very good internal consistency as
one combined measure (=0.90).
Results of the regression analyses partially supported the hypothesis and are
shown in Tables 14a-c. After controlling for pretreatment health-related quality of
life, treatment information communication, and problem-focused coping,
hierarchical regression analyses revealed that measures of pretreatment choice
deliberation did not predict any of the general quality of life outcomes (Table 14a),
but did predict two of the prostate cancer quality of life outcomes (see Table 14b).
91
Table 13
Correlations of Measures of Pretreatment Choice Deliberation and One-
Month Post-Treatment Health-Related Quality of Life (N=57)
Treatment Choice Deliberation
Post-Treatment Health-Related Quality of
Life FITC ITDM PF-Coping
General Quality of Life
FACT-G Summary
General Well Being -0.173 0.301* 0.136
FACT Subscales
Emotional Well Being -0.208 0.327* 0.088
Functional Well Being -0.157 0.287* 0.117
Physical Well Being -0.105 -0.037 -0.102
Social Well Being -0.061 0.276* 0.269*
Prostate Cancer Quality of Life
FACT-P
Prostate Cancer Well Being -0.107 0.014 0.147
PCI
Bowel Functioning -0.110 -0.077 -0.036
Sexual Functioning -0.335* -0.124 0.221
Urinary Functioning -0.231 -0.212 -0.088
Psychosocial Adjustment
Stress: IES-R -0.095 -0.081 0.071
Mood: PANAS
Positive Mood 0.129 0.142 0.130
Negative Mood -0.117 -0.053 0.130
Depression: CES-D -0.036 -0.139 0.027

Notes:
FITC=Factors Influencing Treatment Choice; ITDM=Involvement in Treatment Decision Making;
PF-Coping=Problem-Focused Coping; FACT-G=Functional Assessment of Cancer Therapy, General;

FACT-P=Functional Assessment of Cancer Therapy; PCI=Prostate Cancer Index; IES-R=Impact of
Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies,
Depression Scale.
*p < .05, two-tailed
92
Pretreatment choice deliberation predicted prostate cancer well-being, accounting for
an additional 9% of its variance. Patients’ involvement in treatment decision making
uniquely contributed to post-treatment prostate cancer well-being, suggesting that
greater involvement in decision making at pretreatment was associated with worse
prostate cancer well-being one-month post-treatment (Z=6.36, SE=2.78, t = 2.29,
p=0.026). With respect to post-treatment urinary functioning, measures of choice
deliberation collectively accounted for an additional 13% of its variance, after
controlling for covariates, suggesting that the greater the number of influential
factors in treatment choice and the greater involvement in decision making were
related to worse post-treatment urinary functioning (YF(6,50) = 5.87, p=0.005). With
respect to post-treatment psychosocial adjustment (Table 14c), treatment choice
deliberation did not predict any of these outcome measures. However, a trend in
predicting positive mood was evident, indicating that the greater the number of
factors considered during decision making, the better the positive mood one month
post-treatment ([F = 3.09, p=0.054).
A potential confound to these results might be explained by the association
between problem-focused coping and involvement in treatment decision making.
The correlation between the two measures is significant to the extent to indicate
potential convergent validity (r = 0.45), but not so high as to indicate measuring the
same construct (Westin and Rosenthal, 2003). Yet involvement in treatment decision
making was observed to be related to various quality of life whereas, problem-
93
93
Table 14a
Summary of Hierarchical Regression Analyses of Pretreatment Choice Deliberation, controlling for Pretreatment
Quality of Life, Treatment Information Communication, and Problem-Focused Coping Predicting FACT-G
Summary and Subscale Measures of One-Month Post-Treatment Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
FACT - General Well Being, Post-Treatment
Step 1 (4,52) 0.527 16.61 ***
FACT General, Pretreatment 0.666 ***
Age 0.107
Treatment Information Communication
b
-0.323 *
Problem-Focused Coping 0.182
Step 2 (6,50) 0.525 11.30 *** 0.015 0.860
Factors Influencing Treatment Choice 0.095
Involvement in Treatment Decision Making -0.149
FACT - Emotional Well Being, Post-Treatment
Step 1 (4,52) 0.396 10.18 ***
FACT Emotional, Pretreatment 0.495 ***
Age 0.205
Treatment Information Communication
b
-0.219
Problem-Focused Coping 0.228
Step 2 (6,50) 0.376 6.61 *** 0.003 0.149
Factors Influencing Treatment Choice 0.073
Involvement in Treatment Decision Making -0.027
FACT - Functional Well Being, Post-Treatment
Step 1 (3,53) 0.309 9.35 ***
FACT Functional, Pretreatment 0.487 ***
Treatment Information Communication
b
-0.233
Problem-Focused Coping 0.142
Step 2 (5,51) 0.290 5.58 *** 0.008 0.306
Factors Influencing Treatment Choice -0.105
Involvement in Treatment Decision Making 0.043
94
94
Table 14a (continued)
Summary of Hierarchical Regression Analyses of Pretreatment Choice Deliberation, controlling for Pretreatment
Quality of Life, Treatment Information Communication, and Problem-Focused Coping Predicting FACT-G and
Summary Measures of One-Month Post-Treatment Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
FACT - Physical Well Being, Post-Treatment
Step 1 (3,53) 0.110 3.30 *
FACT Physical, Pretreatment 0.333 *
Treatment Information Communication
b
-0.279 *
Problem-Focused Coping -0.080
Step 2 (5,51) 0.091 2.12 0.014 0.442
Factors Influencing Treatment Choice 0.022
Involvement in Treatment Decision Making -0.125
FACT - Social Well Being, Post-Treatment
Step 1 (3,53) 0.484 18.48 ***
FACT Social, Pretreatment 0.673 ***
Treatment Information Communication
b
0.047
Problem-Focused Coping 0.012
Step 2 (5,51) 0.471 10.96 *** 0.007 0.355
Factors Influencing Treatment Choice 0.100
Involvement in Treatment Decision Making -0.073
Notes: FACT = Functional Assessment of Cancer Therapy.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
b
Treatment Information Communication comprises the summary of Communication with Medical Team, Patient Perceived Understanding and
Patient Appraisal of Physician Communication Effectiveness.
*p < .05. **p < .01. ***p < .001.
95
95
Table 14b
Summary of Hierarchical Regression Analyses of Pretreatment Choice Deliberation, controlling for Pretreatment
Prostate Cancer Quality of Life, Treatment Information Communication, and Problem-Focused Coping Predicting
FACT-P and PCI Measures of One-Month Post-Treatment Prostate Cancer Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value YR
2
Value r
a
FACT - Prostate Cancer Well Being, Post-Treatment
Step 1 (3,53) 0.233 6.69 ***
FACT-P, Pretreatment 0.564 ***
Treatment Information Communication
b
-0.121
Problem-Focused Coping 0.243
Step 2 (5,51) 0.304 5.89 *** 0.092 3.687 *
Factors Influencing Treatment Choice -0.242
Involvement in Treatment Decision Making -0.305 *
PCI - Bowel Functioning, Post Treatment
Step 1 (3,53) 0.044 0.21
FACT-P, Pretreatment 0.131
Treatment Information Communication
b
0.051
Problem-Focused Coping 0.049
Step 2 (5,51) 0.050 0.46 0.032 0.850
Factors Influencing Treatment Choice -0.130
Involvement in Treatment Decision Making -0.144
96
96
Table 14b (continued)
Summary of Hierarchical Regression Analyses of Pretreatment Choice Deliberation, controlling for Pretreatment
Prostate Cancer Quality of Life, Treatment Information Communication, and Problem-Focused Coping Predicting
FACT-P and PCI Measures of One-Month Post-Treatment Prostate Cancer Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value YR
2
Value r
a
PCI - Sexual Functioning, Post Treatment
Step 1 (5,51) 0.229 4.32 **
FACT-P, Pretreatment 0.260
Age -0.311 *
Prostatectomy
c
0.233
Treatment Information Communication
b
0.094
Problem-Focused Coping 0.177
Step 2 (7,49) 0.238 3.51 ** 0.036 1.320
Factors Influencing Treatment Choice 0.156
Involvement in Treatment Decision Making -0.146
PCI - Urinary Functioning, Post Treatment
Step 1 (4,52) 0.267 6.10 ***
FACT-P, Pretreatment 0.242
Prostatectomy
c
0.558 ***
Treatment Information Communication
b
0.046
Problem-Focused Coping 0.276
Step 2 (6,50) 0.382 6.78 *** 0.129 5.869 **
Factors Influencing Treatment Choice -0.349 *
Involvement in Treatment Decision Making -0.340 *
Notes: FACT-P = Functional Assessment of Cancer Therapy, Prostate Cancer Specific; PCI = Prostate Cancer Index.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the equation.
b
Treatment Information Communication comprises the summary of Communication with Medical Team, Patient Perceived Understanding and Patient
Appraisal of Physician Communication Effectiveness.
c
Prostatectomy was coded as 0 = Having had prostatectomy, 1 = Not having had prostatectomy.
*p < .05. **p < .01. ***p < .001.
97
97
Table 14c
Summary of Hierarchical Regression Analyses of Pretreatment Choice Deliberation, controlling for Pretreatment
Psychological Adjustment, Treatment Information Communication, and Problem-Focused Coping Predicting One-
Month Post-Treatment Psychological Adjustment (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
IES-R: Stress, Post Treatment
Step 1 (3,53) 0.020 0.64
IES-R, Pretreatment 0.169
Treatment Information Communication
b
0.097
Problem-Focused Coping -0.041
Step 2 (5,51) 0.037 0.60 0.021 0.568
Factors Influencing Treatment Choice -0.148
Involvement in Treatment Decision Making -0.014
PANAS-P: Positive Mood, Post Treatment
Step 1 (3,53) 0.006 1.12
PANAS-P, Pretreatment -0.012
Treatment Information Communication
b
-0.271 *
Problem-Focused Coping 0.038
Step 2 (5,51) 0.079 1.96 0.102 3.098
Factors Influencing Treatment Choice 0.328 *
Involvement in Treatment Decision Making 0.021
98
98
Table 14c (continued)
Summary of Hierarchical Regression Analyses of Pretreatment Choice Deliberation, controlling for Pretreatment
Psychological Adjustment, Treatment Information Communication, and Problem-Focused Coping Predicting One-
Month Post-Treatment Psychological Adjustment (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
PANAS-N: Negative Mood, Post Treatment
Step 1 (3,53) 0.009 0.83
PANAS-N, Pretreatment 0.169
Treatment Information Communication
b
0.169
Problem-Focused Coping -0.146
Step 2 (5,51) 0.007 0.93 0.039 1.076
Factors Influencing Treatment Choice -0.170
Involvement in Treatment Decision Making 0.092
CES-D: Depression, Post Treatment
Step 1 (3,53) 0.039 1.76
CES-D, Pretreatment 0.238
Treatment Information Communication
b
0.115
Problem-Focused Coping 0.068
Step 2 (5,51) 0.023 1.27 0.020 0.571
Factors Influencing Treatment Choice -0.138
Involvement in Treatment Decision Making -0.044
Notes: IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression Scale.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the equation.
b
Treatment Information Communication comprises the summary of Communication with Medical Team, Patient Perceived Understanding and
Patient Appraisal of Physician Communication Effectiveness.
*p < .05. **p < .01. ***p < .001.
99
focused coping was not. Having included problem-focused coping in the regression
equations to control for its effect on post-treatment quality of life outcomes may
have produced outcomes that do not correctly reflect the nature of the relationships
between the choice deliberation process and post-treatment quality of life. To this
end, two approaches to exploratory analyses were conducted. The first approach
considered whether problem-focused coping served to mediate the relationships
between choice deliberation process and post-treatment quality of life. This
speculation arises from the literature concerning how coping strategies often serve to
buffer post-treatment functioning in patients (Carver et al., 1993). It may be possible
that problem-focused coping affects the choice deliberation process in this manner;
therefore, further exploratory hierarchical regressions were conducted in order to
observe whether problem-focused coping mediated the relationship between choice
deliberation and post-treatment quality of life. However, these post-hoc exploratory
data revealed that problem-focused coping did not mediate any of the relationships
between choice deliberation variables and post-treatment health-related quality of
life outcomes.
The second approach considered the possibility that including problem-
focused coping as a control variable overestimated its association with both the
choice deliberation process and post-treatment quality of life. Therefore, a series of
post-hoc exploratory hierarchical regression analyses was rerun having removed
problem-focused coping from these equations. These outcome data revealed that the
100
choice deliberation process, after controlling for treatment information
communication and pretreatment health-related quality of life, did not account for
any of the post-treatment quality of life outcomes. The fact that two outcomes
concerning prostate cancer quality of life were no longer predicted by choice
deliberation after removing problem-focused coping from the equations is suggestive
that problem-focused coping could have served as a suppressor variable affecting
choice deliberation’s effect on post-treatment prostate cancer quality of life
(Pedhazur, 1997).
Hypothesis 2 – Treatment Choice Appraisal, Satisfaction
Linear regression analyses were conducted in order to test the hypothesis that
consonance in treatment decision making would predict satisfaction with treatment
choice at pretreatment and one-month post-treatment. Given that 44 participants’
(77%) composite scores indicated that patients had engaged in the treatment decision
making process to the extent that they wished, the composite variable was recoded as
a dichotomous variable (score of 0=consonance and 1=dissonance) in order to
correct for the biased distribution (Pedhauzer, 1997). Results of these two
regressions did not support the hypotheses and are listed in Table 15. Treatment
decision making consonance did not significantly account for the variance in
treatment satisfaction at either time point (p=0.423 and p=0.343, respectively).
Due to the asymmetrical distribution of consonance scores, post-hoc
independent samples t-tests were conducted to observe whether patients who were
101
consonant in their treatment decision making reported higher levels of satisfaction
with choice in comparison to those who were dissonant. Although men who were
consonant in the treatment decision making appeared to have higher mean scores in
satisfaction at both time points (respective consonant and dissonant patient means at
Time 1 were 12.50 and 11.92, and at Time 2 were 12.84 and 12.23), t-test results
indicated that these scores were not different in pretreatment satisfaction (t(55) =
0.81, p=0.931) nor post-treatment satisfaction (t(55) = 0.96, p=0.075). The dissonant
group was split into two groups of (1) those patients who wished they had had more
decision making autonomy (n=2) and (2) those patients who wished they did not
have as much autonomy (n=11) in order to observe whether the means in these two
groups were discernibly different. The means for each group at either time point
were not appreciably dissimilar (means at Time 1 were 12.00 and 11.91, and at Time
2 were 12.00 and 12.27, for groups 1 and 2, respectively). Analyses of variance
further indicated that the mean scores did not differ significantly between the three
groups at either time point (p=0.727 and p=0.630, respectively).
It is possible that the restricted range of consonance scores may have
contributed to the lack of findings. The three-item scale used to measure satisfaction
with treatment decision making involvement also is a measure that captures patients’
reports of whether they participated in the treatment decision making process to the
extent that they wished. Given that the two measures of treatment decision making
Consonance and satisfaction with treatment decision making involvement have
102
Table 15
Summary of Linear Regression Analyses for Consonance in Treatment Decision
Making Predicting Satisfaction with Treatment Choice at Pretreatment and One-
Month Post- Treatment (N=56)
df Adj F Equation ( SE Z beta
Measure R2 Value
Pretreatment SWC (1,55) 0.006 0.65
TDMC
a
-0.580 0.720 -0.110
Post-Treatment SWC (1,55) 0.001 0.92
TDMC -0.610 0.640 -0.130
Table 15a
Summary of Regression Analyses for Satisfaction with Decision Making
Involvement Predicting Satisfaction with Treatment Choice at Pretreatment and
One-Month Post Treatment (N=56)
df Adj F Equation ( SE Z beta
Measure R2 Value

Pretreatment SWC (1,55) 0.118 8.46 **
SWI 2.677 0.921 0.365 **
Post-Treatment SWC (1,55) 0.094 6.81 *
SWI 2.175 0.834 0.332 *
Notes: SWC=Satisfaction with Treatment Choice; TDMC=Treatment Decision Making Consonance;
SWI=Satisfaction with Decision Making Involvement.

a
TDMC was coded as 0 = Consonant with treatment choice, 1 = Dissonant with treatment choice.
*p < .05. **p<.01.

103
similar meanings, regression analyses were performed, post-hoc, using satisfaction
with treatment decision making involvement to predict satisfaction with treatment
choice at both time points. These findings also are listed in Table 15 (sub-headed as
Table 15a) and demonstrated that satisfaction with treatment decision making
involvement significantly accounted for 12% of the variance in pretreatment
satisfaction with treatment choice and it accounted for 9% of the variance in one-
month post-treatment satisfaction with treatment choice.
Hypothesis 3 – Treatment Choice Appraisal, Expectations
Table 16 lists the bivariate correlations of the measures of treatment choice
appraisal and post-treatment health-related quality of life variables. Results indicated
that few measures of treatment choice appraisal related to post-treatment quality of
life. However, patterns concerning satisfaction with treatment choice and treatment-
specific optimism were noted. The data indicated that greater satisfaction with
treatment choice was related to better general and emotional well-being, but with less
post-treatment positive mood. With respect to treatment-specific optimism, the data
indicated that greater treatment-specific optimism related to better emotional well-
being, but worse prostate cancer well-being and sexual functioning.
Hierarchical regression analyses were conducted to test the hypothesis that
pretreatment treatment-specific optimism would predict health-related quality of life
at one-month post-treatment, after controlling for pretreatment quality of life and
selected age and surgery covariates. The hypothesis also stated that the relationship
104
Table 16
Correlations of Measures of Pretreatment Choice Appraisal and Post-Treatment
Health-Related Quality of Life (N=57)†

Post-Treatment Health-Related Quality
of Life TDMC
a
SWC SWI TSO LOT
General Quality of Life
FACT-G Summary
General Well Being 0.062 0.303* 0.122 0.018 0.243
FACT Subscales
Emotional Well Being 0.013 0.380** 0.135 0.292* 0.061
Functional Well Being -0.045 0.238 0.143 -0.165 0.309*
Physical Well Being -0.070 0.152 -0.034 0.236 0.057
Social Well Being -0.037 0.189 0.032 0.145 0.209
Prostate Cancer Quality of Life
FACT-P
Prostate Cancer Well Being -0.046 -0.028 -0.039 -0.265* 0.091
PCI
Bowel Functioning -0.026 0.205 -0.185 0.046 -0.038
Sexual Functioning 0.186 -0.122 -0.267* -0.273* -0.014
Urinary Functioning -0.106 0.014 -0.216 0.216 0.058
Psychosocial Adjustment
Stress: IES-R 0.034 -0.012 -0.086 -0.086 0.121
Mood: PANAS
Positive Mood 0.062 -0.276* -0.149 0.149 0.039
Negative Mood 0.099 0.066 -0.079 0.079 0.003
Depression: CES-D 0.042 0.119 -0.192 0.192 -0.039

Notes:
TDMC=Treatment Decision Making Consonance; SWC=Satisfaction with Choice; SWI=Satisfaction with
Decision Making Involvement; TSO=Treatment-Specific Optimism; LOT=Life Orientation Test
(dispositional optimism); FACT-G=Functional Assessment of Cancer Therapy, General; FACT-P=
Functional Assessment of Cancer Therapy; PCI=Prostate Cancer Index; IES-R=Impact of Events Scale;
PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression
Scale.
a
TDMC was coded as 0 = Consonant with treatment choice, 1 = Dissonant with treatment choice.
†
For the variable, TDMC, Spearman's rho correlations were conducted. All other variables were analyzed
with Pearson product-moment correlations.
*p < .05, **p<.01, two-tailed

105
between treatment-specific optimism and quality of life would be mediated by
problem-focused coping. Prior to ascertaining the applicability of conducting
mediational analyses, the appropriate correlational and regression analyses were
conducted to observe the nature of the relationship between treatment-specific
optimism and coping. Treatment-specific optimism and coping did not correlate
significantly (r = 0.06, p=0.672), nor did treatment-specific optimism predict
problem-focused coping in a linear regression (F(1,55) = 0.18, p=0.672). Therefore
no mediational analyses were performed.
Results of the regression analyses partially supported the hypothesis and are
shown in Tables 17a-c. Treatment-specific optimism was found to significantly
predict two post-treatment quality of life outcomes. After controlling for
pretreatment emotional well-being and age, treatment-specific optimism made a
significant contribution to the variance in post-treatment emotional well-being,
accounting for an additional 6.5% of its variance; the direction of the relationship
indicating that greater treatment-specific optimism was associated with an
improvement in emotional well-being one-month post-treatment (Table 17a).
Treatment-specific optimism also predicted the one-month outcome of prostate
cancer quality of life. After controlling for pretreatment prostate cancer quality of
life, treatment-specific optimism accounted for an additional 5.6% of the variance in
prostate cancer quality of life at one-month post-treatment, indicating that greater
106
Table 17a
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, Controlling for
Pretreatment Quality of Life, Predicting FACT-G Summary and Subscale Measures of One-Month Post-Treatment
Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
FACT - General Well Being, Post-Treatment
Step 1 (2,54) 0.498 28.74 ***
FACT General, Pretreatment 0.691 ***
Age 0.074
Step 2 (3,53) 0.488 18.81 *** 0.000 0.012
Treatment-Specific Optimism -0.013
FACT - Emotional Well Being, Post-Treatment
Step 1 (2,54) 0.314 13.85 ***
FACT Emotional, Pretreatment 0.493 ***
Age 0.171
Step 2 (3,53) 0.371 11.98 *** 0.065 5.805 *
Treatment-Specific Optimism 0.314 *
FACT - Functional Well Being, Post-Treatment
Step 1 (1,55) 0.263 20.97 ***
FACT Functional, Pretreatment 0.542 ***
Step 2 (2,54) 0.288 12.32 *** 0.037 2.928
Treatment-Specific Optimism -0.193
FACT - Physical Well Being, Post-Treatment
Step 1 (1,55) 0.058 4.45 *
FACT Physical, Pretreatment 0.276 *
Step 2 (2,54) 0.095 3.94 * 0.052 3.246
Treatment-Specific Optimism -0.238
107
Table 17a (continued)
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, Controlling for
Pretreatment Quality of Life, Predicting FACT-G Summary and Subscale Measures of One-Month Post-Treatment
Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
FACT - Social Well Being, Post-Treatment
Step 1 (1,55) 0.497 56.42 ***
FACT Social, Pretreatment 0.707 ***
Step 2 (2,54) 0.493 28.19 *** 0.004 0.490
Treatment-Specific Optimism 0.095
Notes:
FACT=Functional Assessment of Cancer Therapy.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
*p < .05. **p < .01. ***p < .001.
108
Table 17b
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, Controlling for
Pretreatment Prostate Cancer Quality of Life, Predicting FACT-P and PCI Measures of One-Month Post-Treatment
Prostate Cancer Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
FACT-P - Prostate Cancer Well Being, Post-
Treatment
Step 1 (1,55) 0.258 20.46 ***
FACT-P, Pretreatment 0.525 ***
Step 2 (2,54) 0.302 13.10 *** 0.056 4.451 *
Treatment-Specific Optimism -0.276 *
PCI - Bowel Functioning, Post-Treatment
Step 1 (1,55) 0.010 0.44
FACT-P, Pretreatment 0.087
Step 2 (2,54) 0.027 0.26 0.002 0.093
Treatment-Specific Optimism -0.042
PCI - Sexual Functioning, Post-Treatment
Step 1 (3,53) 0.192 5.44 **
FACT-P, Pretreatment 0.244
Age -0.411 **
Prostatectomy
c
Step 2 (4,52) 0.220 4.94 ** 0.040 2.870
Treatment-Specific Optimism -0.229
109
Table 17b (continued)
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, Controlling for
Pretreatment Prostate Cancer Quality of Life, Predicting FACT-P and PCI Measures of One-Month Post-Treatment
Prostate Cancer Quality of Life (N=56)
Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
PCI - Urinary Functioning, Post-Treatment
Step 1 (2,54) 0.291 12.49 ***
FACT-P, Pretreatment 0.153
Prostatectomy
c
0.517 ***
Step 2 (3,53) 0.279 8.21 *** 0.001 0.067
Treatment-Specific Optimism -0.036
Notes:
FACT-P=Functional Assessment of Cancer Therapy, Prostate Cancer Specific; PCI=Prostate Cancer Index.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
b
Prostatectomy was coded as 0 = Having had prostatectomy, 1 = Not having had prostatectomy.
*p < .05. **p < .01. ***p < .001.
110
Table 17c
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific
Optimism, Controlling for Pretreatment Psychosocial Adjustment, Predicting One-
Month Post-Treatment Psychosocial Adjustment (N=56)

Adj F YF Partial
Measure df R2 Value
YR
2
Value
r
a
IES-R: Stress, Post Treatment
Step 1 (1,55) 0.004 1.23
IES-R, Pretreatment 0.159
Step 2 (2,54) 0.003 0.91 0.011 0.595
Treatment-Specific Optimism -0.104
PANAS-P: Positive Mood, Post Treatment
Step 1 (1,55) 0.012 0.36
PANAS-P, Pretreatment 0.097
Step 2 (2,54) 0.005 0.87 0.025 1.383
Treatment-Specific Optimism -0.158
PANAS-N: Negative Mood, Post Treatment
Step 1 (1,55) 0.004 0.80
PANAS-N, Pretreatment 0.108
Step 2 (2,54) 0.019 0.47 0.003 0.157
Treatment-Specific Optimism -0.054
CES-D: Depression, Post Treatment
Step 1 (1,55) 0.060 4.59 *
CES-D, Pretreatment 0.283 *
Step 2 (2,54) 0.081 3.47 * 0.037 2.245
Treatment-Specific Optimism 0.192
Notes: IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for
Epidemiological Studies, Depression Scale
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable,
controlling for all other predictors in the equation.
*p < .05. **p < .01. ***p < .001.
111
treatment-specific optimism was associated with worse prostate cancer well-being
(Table 17b). Treatment-specific optimism was not found to predict any other health-
related quality of life measure. However, there were three instances where treatment-
specific optimism trended toward predicting post-treatment outcomes. With respect
to functional and physical well-being, treatment-specific optimism accounted for an
additional 4% and 5% of these variances at a respective p=0.093 and p=0.077 level
(see Table 17a). And with respect to sexual functioning, after controlling for age and
surgery, an additional 4% of its variance was accounted for by treatment-specific
optimism at the p=0.096 level (see Table 17b). In all three conditions treatment-
specific optimism was negatively related to these outcomes, indicating that greater
treatment-specific optimism was related to worse functional, physical and sexual
well-being at one-month post-treatment.
Question 2 – Treatment Choice Appraisal, Expectations
Hierarchical regression analyses were conducted to test the exploratory
question whether treatment-specific optimism would predict health-related quality of
life above and beyond that of dispositional optimism. Results are listed in Tables
18a-c. The findings revealed that after controlling for pretreatment emotional well-
being, age, and dispositional optimism, treatment-specific optimism accounted for an
additional 6.3% of the variance in one-month post-treatment emotional well-being,
indicating that greater treatment-specific optimism was associated with better
emotional well-being (see Table 18a). Treatment-specific optimism also predicted
112
one-month post-treatment prostate cancer quality of life after controlling for
pretreatment prostate cancer quality of life and dispositional optimism, accounting
for an additional 5.5% of the variance; the data indicated that greater treatment-
specific optimism was associated with worse prostate cancer quality of life (see
Table 18b). With respect to physical well-being and sexual functioning, treatment-
specific optimism was found to trend toward accounting for additional variances in
these two outcomes. After controlling for covariates, treatment-specific optimism
uniquely accounted for 5.3% of the variance in physical well-being at the p=0.080
level, and for 4% of the variance in sexual functioning at the p=0.097 level; the
nature of these relationships indicated that greater treatment-specific optimism was
associated with worse physical and sexual functioning.
Exploratory Findings
The original analyses discussed above were conceived and conducted to
observe how the processes of treatment decision making contributed to the change in
quality of life over a one-month period. Only those variables thought to confound
quality of life outcomes (i.e., age and surgery) were controlled for in some of the
predictive models in order to appreciate the effects of the treatment decision making
process uniquely. However, results of bivariate correlations (as listed in Table 6)
revealed that medical and demographic variables also correlated with variables of the
treatment decision making process, suggesting that patients’ experience of the
treatment decision making process may vary as a function of their treatment
113
Table 18a
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, controlling for
Pretreatment Dispositional Optimism and General Quality of Life, Predicting FACT-G Summary and Subscale
Measures One-Month Post-Treatment General Quality of Life (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
FACT - General Well Being, Post-Treatment
Step 1 (2,54) 0.498 28.74 ***
FACT General, Pretreatment 0.679 ***
Age 0.069
Step 2 (3,53) 0.500 19.70 *** 0.012 1.298
Life Orientation Test 0.154
Step 3 (4,52) 0.491 14.50 *** 0.000 0.000
Treatment-Specific Optimism 0.000
FACT - Emotional Well Being, Post-Treatment
Step 1 (2,54) 0.314 13.85 ***
FACT Emotional, Pretreatment 0.490 ***
Age 0.172
Step 2 (3,53) 0.304 9.16 *** 0.002 0.188
Life Orientation Test 0.028
Step 3 (4,52) 0.359 8.38 *** 0.063 5.538 *
Treatment-Specific Optimism 0.310 *
FACT - Functional Well Being, Post-Treatment
Step 1 (1,55) 0.263 20.97 ***
FACT Functional, Pretreatment 0.550 ***
Step 2 (2,54) 0.330 14.78 *** 0.078 6.490 *
Life Orientation Test 0.316 *
Step 3 (3,53) 0.347 10.92 *** 0.028 2.423
Treatment-Specific Optimism -0.209
114
Table 18a (continued)
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, controlling for
Pretreatment Dispositional Optimism and General Quality of Life, Predicting FACT-G Summary and Subscale
Measures One-Month Post-Treatment General Quality of Life (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
FACT - Physical Well Being, Post-Treatment
Step 1 (1,55) 0.058 4.45 *
FACT Physical, Pretreatment 0.274 *
Step 2 (2,54) 0.041 2.19 0.000 0.002
Life Orientation Test -0.014
Step 3 (3,53) 0.078 2.58 0.053 3.198
Treatment-Specific Optimism -0.238
FACT - Social Well Being, Post-Treatment
Step 1 (1,55) 0.497 56.42 ***
FACT Social, Pretreatment 0.697 ***
Step 2 (2,54) 0.498 28.82 *** 0.010 1.108
Life Orientation Test 0.154
Step 3 (3,53) 0.495 19.32 *** 0.006 0.672
Treatment-Specific Optimism 0.112
Notes:
FACT=Functional Assessment of Cancer Therapy.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
*p < .05. **p < .01. ***p < .001.
115
Table 18b
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, controlling for
Pretreatment Dispositional Optimism and Prostate Cancer Well-being, Predicting FACT-P and PCI Measures of One-
Month Post-Treatment Prostate Cancer Well-being (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
FACT - Prostate Cancer Well Being, Post-Treatment
Step 1 (1,55) 0.258 20.46 ***
FACT-P, Pretreatment 0.522 ***
Step 2 (2,54) 0.245 10.08 *** 0.001 0.053
Life Orientation Test 0.009
Step 3 (3,53) 0.289 8.57 *** 0.055 4.317 *
Treatment-Specific Optimism -0.274 *
PCI - Bowel Functioning
Step 1 (1,55) 0.010 0.44
FACT-P, Pretreatment 0.083
Step 2 (2,54) 0.028 0.24 0.001 0.040
Life Orientation Test 0.024
Step 3 (3,53) 0.046 0.18 0.002 0.083
Treatment-Specific Optimism -0.039
PCI - Sexual Functioning
Step 1 (3,53) 0.192 0.54 **
FACT-P, Pretreatment 0.245
Age -0.407 **
Prostatectomy
b
0.108
Step 2 (4,52) 0.177 4.00 ** 0.000 0.001
Life Orientation Test -0.027
Step 3 (5,51) 0.205 3.89 ** 0.041 2.856
Treatment-Specific Optimism -0.230
116
Table 18b (continued)
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific Optimism, controlling for
Pretreatment Dispositional Optimism and Prostate Cancer Well-being, Predicting FACT-P and PCI Measures of One-
Month Post-Treatment Prostate Cancer Well-being (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
PCI - Urinary Functioning
Step 1 (2,54) 0.291 12.49 ***
FACT-P, Pretreatment 0.163
Prostatectomy
b
0.516 ***
Step 2 (3,53) 0.284 8.42 *** 0.006 0.502
Life Orientation Test -0.094
Step 3 (4,52) 0.271 6.21 *** 0.001 0.038
Treatment-Specific Optimism -0.027
Notes: FACT-P = Functional Assessment of Cancer Therapy, Prostate Cancer Specific; PCI = Prostate Cancer Index.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion variable, controlling for all other predictors in the
equation.
b
Prostatectomy was coded as 0 = Having had prostatectomy, 1 = Not having had prostatectomy.
*p < .05. **p < .01. ***p < .001.
117
Table 18c
Summary of Hierarchical Regression Analyses for Variables of Treatment-Specific
Optimism, Controlling for Pretreatment Dispositional Optimism and Psychosocial
Adjustment, Predicting One-Month Post-Treatment Psychosocial Adjustment (N=56)
Adj F YF Partial
Measures df R2 Value
YR
2
Value
r
a
IES-R: Stress, Post Treatment
Step 1 (1,55) 0.004 1.23
IES-R, Pretreatment 0.180
Step 2 (2,54) 0.008 1.23 0.022 1.224
Life Orientation Test 0.143
Step 3 (3,53) 0.002 0.97 0.009 0.483
Treatment-Specific Optimism -0.095
PANAS-P: Positive Mood, Post Treatment
Step 1 (1,55) 0.012 0.36
PANAS-P, Pretreatment 0.093
Step 2 (2,54) 0.030 0.19 0.000 0.024
Life Orientation Test 0.003
Step 3 (3,53) 0.024 0.57 0.024 1.334
Treatment-Specific Optimism -0.157
PANAS-N: Negative Mood, Post Treatment
Step 1 (1,55) 0.004 0.80
PANAS-N, Pretreatment 0.110
Step 2 (2,54) 0.022 0.40 0.000 0.021
Life Orientation Test 0.023
Step 3 (3,53) 0.038 0.32 0.003 0.162
Treatment-Specific Optimism -0.055
CES-D: Depression, Post Treatment
Step 1 (1,55) 0.060 4.59 *
CES-D, Pretreatment 0.299 *
Step 2 (2,54) 0.049 2.46 0.006 0.375
Life Orientation Test 0.105
Step 3 (3,53) 0.074 2.49 0.040 2.433
Treatment-Specific Optimism 0.210
Notes: IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale;
CES-D=Centers for Epidemiological Studies, Depression Scale.
a
The r displayed is the partial r, representing the relation of the predictor and the criterion
variable, controlling for all other predictors in the equation.
*p < .05. **p < .01. ***p < .001.
118
modality, age, or disease severity. Having not taken into consideration the nature of
these relationships or how they associated with post-treatment quality of life possibly
could have yielded conclusions that overlooked patients’ true experiences. Therefore,
descriptive and moderational exploratory analyses were conducted to test the
relationships among the processes of treatment decision making,
medical/demographic factors, and post-treatment health-related quality of life.
Given that these analyses are strictly exploratory, alphas were set to p<0.10
to broaden the scope of considering potential relationships that otherwise could be
lost due to the small sample sizes. Moreover, concerning the small sample size,
pretreatment health-related quality of life measures were not controlled for in these
post-hoc analyses as they were for the apriori questions and hypotheses of the study.
Analyzing how pretreatment medical, demographic, and treatment decision making
factors would relate to post-treatment quality of life outcomes, without controlling
for pretreatment quality of life, was anticipated to provide a broader clinical
description of patients’ quality of life at the one-month post-treatment period.
The first step in these analyses was to describe the data by (1) observing
partial correlations of how the medical and demographic factors uniquely related to
treatment decision making processes and by (2) assessing whether patients’
experiences in the treatment decision making process differed across groups. Based
on these observations and those from the original analyses, the second step was to
examine the data by (3) analyzing which factors moderated the treatment decision
119
making process or patient post-treatment quality of life. These moderational analyses
were examined through conducting hierarchical regressions using the centered cross-
products of the variables of interest in order to create the interaction terms (Aiken &
West, 1991) thought to contribute to patients’ participation in the treatment decision
making process or to their post-treatment quality of life. The four sets of
moderational analyses conducted were to test whether treatment modality interacted
with (1) age or (2) disease severity in predicting patients’ participation in the
treatment decision making process, in addition to test whether treatment modality
interacted with (3) involvement in decision making or (4) treatment-specific
optimism in predicting post-treatment health-related quality of life. Any meaningful
findings resulting from these exploratory analyses are considered tentative.
Partial Correlations
Table 19 lists the results of the exploratory partial correlations conducted to
assess the unique relationships between age, treatment modality (comprising surgery,
radiation, and ADT), and disease severity with the variables of the treatment decision
making process, after controlling for each of the five covariates in turn. By
comparing the zero-order correlations in Table 6 and these partial correlations in
Table 19, one can observe that after controlling for disease severity and treatment
modality, age tended to lose its significant correlations with the factors of treatment
choice deliberation, but maintained its positive correlation with appraisal of
physician communication, indicating that older age was related to higher appraisal.
120
With respect to Gleason scores and PSA values, a pattern was evident in which the
data revealed PSA values having gained relationships with measures of treatment
information communication and of satisfaction, indicating that higher PSA was
related to lower levels of treatment information communication and less satisfaction
with both treatment choice and decision making involvement. Likewise, higher
Gleason score was associated with less treatment-specific optimism and approached
a relationship with factors influencing treatment choice, indicating that higher
Gleason score was related to less factors having influence on one’s choice. With
respect to treatment modality, both surgery and radiation gained relationships with
variables of treatment information communication and treatment-specific optimism,
suggesting that patients who received those treatments reported receiving greater
amounts of information, rated their physicians’ communication higher, and reported
having greater treatment-specific optimism. The data also revealed that higher
involvement in treatment decision making was associated with having received
surgery.
Analyses of Variance
The exploratory correlational data appear to suggest that the treatment
decision making process could have been experienced differently by patients as a
function of their age, disease severity, or their treatment choice. Therefore further
analyses were conducted considering these factors in relation patients’ experiences.
121
Table 19
Partial Correlations of Demographic/Medical Co-Variates and Pretreatment Predictor Variables (N=57)
a
Age Gleason PSA
Had
Surgery
b
Had
Radiation
b
Had
ADT
b
Predictor Variables, Pretreatment
Treatment Information Communication
Communication with Medical Team 0.042 -0.105 -0.393** -0.372** -0.379** -0.229
Patient Perceived Understanding 0.165 -0.010 -0.279* 0.026 -0.112 0.183
PACE 0.378** 0.106 -0.353** -0.349* -0.240† -0.121
Treatment Choice Deliberation
Factors Influencing Treatment Choice -0.224 -0.254† 0.119 0.101 0.134 -0.001
Involvement in Treatment Decision Making 0.056 0.023 -0.143 -0.272* -0.125 -0.185
Problem-Focused Coping -0.250† -0.117 0.158 -0.131 0.052 -0.040
Treatment Choice Appraisal
Treatment Decision Making Consonance 0.010 0.250† 0.169 0.041 0.238† 0.075
Satisfaction with Choice 0.162 0.125 -0.223† 0.026 0.108 0.178
Satisfaction with Decision Making Involvement 0.186 -0.067 -0.283* -0.256† -0.121 -0.063
Treatment-Specific Optimism 0.161 -0.318* -0.055 -0.552*** -0.507*** -0.043
Life Orientation Test 0.095 -0.088 0.213 -0.024 -0.044 -0.066
Note:
PACE=Patient Appraisal of Physician Communication Effectiveness
a
Partial correlations were computed for each variable and controlling for the remaining 5 variables in turn.
b
Treatments for Surgery, Radiation or Hormones were coded as 0 = Having had that procedure, 1 = Not having had that procedure.
†p<.10, *p < .05, **p<.01, ***p<.001, two-tailed
122
To assess whether patients differed in their treatment decision making
processes based on their treatment modality, age, or disease severity, exploratory
analyses of variance (ANOVA) were conducted. To establish comparative groups,
age, Gleason scores, and PSA values were dichotomized into two groups based on
median-score splits. Figures 3-5 provide a graphical illustration of the mean scores
on measures of treatment decision making processes by these groups. Results of the
ANOVAs revealed that few of the treatment decision making processes were
experienced differently when compared by the medical characteristics or age.
However, some significant differences and trends were observed. For instance, with
respect to measures of choice deliberation process (Figure 3), patients with lower
Gleason scores reportedly considered a greater number of influential factors during
their decision making than were men with higher Gleason scores (F(1,55) = 4.82,
p=0.032). And problem-focused coping was utilized differently across comparative
groups. Patients with higher PSA used less problem-focused coping than those with
lower PSA (F(1,55) = 6.11, p=0.017); this difference trends in similar fashion with
those diagnosed with higher Gleason scores. Trends in differences also are observed
with older men utilizing less problem-focused coping (F(1,55) = 3.18, p=0.080). And
as indicated by Scheffe post-hoc analyses, radiation patients trended toward using
less problem-focused coping in comparison to surgery patients (F(1,55) = 2.72,
p=0.076). With respect to measures of treatment information communication (Figure
4), patients with lower Gleason scores reported receiving greater amounts of
123
treatment information in comparison to those with higher scores (F(1,55) = 5.78,
p=0.020), and older patients were more likely to report higher appraisal of physician
communication than younger patients (F(1,55) = 6.88, p=0.011). The only significant
group difference observed in measures of treatment choice appraisal (Figure 5) was
between the three treatment modalities and treatment-specific optimism. The data
revealed that hormone patients reported having less treatment-specific optimism in
comparison to both radiation and surgery patients (F(2,50 = 11.01, p<0.001), as
confirmed by Scheffe post-hoc analyses.
Correlations of Treatment Modality
Although few differences are found in treatment decision making processes
between patients in terms of their age, disease severity, and treatment, possible
interactions may exist between treatment modality and age or severity of disease that
in turn may affect patient experiences in their treatment decision making process. It
might be possible that because age and disease severity are often taken into
consideration when selecting treatments for prostate cancer (Pickett et al., 2000),
these factors may interact with treatment choice in ways that could affect patients’
involvement in the treatment decision making process. To this end, we set out to
observe whether these patterns of association were evident during the processes of
treatment decision making, and whether treatment modality served to moderate these
relationships.

124
Figure 3
Means Scores for Measures of the Choice Deliberation Process by Medical
Characteristics and Age
36.7
34.4
36.0
35.5
35.3
35.8
35.0
36.2
33.8
30.0
31.0
32.0
33.0
34.0
35.0
36.0
37.0
38.0
39.0
40.0
Involvement in Decision Making
(range 8-40)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Involvement in Treatment Decision Making by
Medical and Demographic Characteristics
7.5
6.4
7.8 7.8
6.7
7.5
7.0
7.8
6.1
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
Factors Influencing Treatment Choice
(range 0-13)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Influencing Factors in Choosing Treatment by
Medical and Demographic Characteristics
23.2
20.1
21.3
22.7
20.7
23.0
20.3
22.4
20.1
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
Problem-Focused Coping
(range 0-30)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Problem-Focused Coping by
Medical and Demographic Characteristics
Notes: Sample sizes for Treatment Modality are: Surgery (n=24), Radiation (n=18), and Hormones (n=11); for
Age: 61 or younger (n=28) and 62 or older (n=29); for PSA: 5.8 or lower (n=29) and 5.85 or higher (n-28); for
Gleason Scores: 6 or less (n=39) and 7 or greater (n=18). Significant F-values for group differences below alpha
of 0.10 are indicated; alphas above the 0.10 cut-off are not listed.
p =0.062 p =0.017 p =0.080 p =0.076
p =0.032
125
Figure 4
Means Scores for Measures of Treatment Information Communication by Medical
Characteristics and Age
54.1
55.5
52.0
54.0
52.7
53.7
52.9
55.2
49.2
47.0
47.5
48.0
48.5
49.0
49.5
50.0
50.5
51.0
51.5
52.0
52.5
53.0
53.5
54.0
54.5
55.0
55.5
56.0
Communication with Medical Team
(range 10-70)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Amount of Information Communicated by Medical Team by
Medical and Demographic Characteristics
17.1
17.8
16.7
17.2
17.4
17.2
17.4
17.5
16.9
15.0
15.5
16.0
16.5
17.0
17.5
18.0
18.5
Patient Perceived Understanding
(range 3-21)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Patient Perceived Understanding of Treatment Information by
Medical and Demographic Characteristics
31.2 31.2
29.8
28.7
32.1
29.7
31.2
30.4
30.5
25.5
26.0
26.5
27.0
27.5
28.0
28.5
29.0
29.5
30.0
30.5
31.0
31.5
32.0
32.5
33.0
Patient Appraisal of Physician
Communication
(range 5-35)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Patient Appraisal of Physician Communication Effectiveness by
Medical and Demographic Characteristics
Notes: Sample sizes for Treatment Modality are: Surgery (n=24), Radiation (n=18), and Hormones (n=11); for
Age: 61 or younger (n=28) and 62 or older (n=29); for PSA: 5.8 or lower (n=29) and 5.85 or higher (n-28); for
Gleason Scores: 6 or less (n=39) and 7 or greater (n=18). Significant F-values for group differences below alpha
of 0.10 are indicated; alphas above the 0.10 cut-off are not listed.
p =0.020
p =0.011
126
Figure 5
Means Scores for Measures of the Treatment Choice Appraisal by Medical Characteristics and Age
12.5
12.1
12.5
11.9
12.8
12.3
12.5
12.3
12.5
11.0
11.5
12.0
12.5
13.0
13.5
Satisfied with Treatment Choice
(range 3-15)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Satisfied with Treatment Choice by
Medical and Demographic Characteristics
14.0
13.5
13.8
13.2
13.9
13.4
13.7
13.9
12.8
12.0
12.5
13.0
13.5
14.0
14.5
15.0
Satisfied with Decision Making
(range 3-15)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Satisfied with Decision Making Involvement by
Medical and Demographic Characteristics
16.5
15.4
12.5
14.9
15.2
15.1 15.1
15.4
14.3
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
17.5
Treatment Specific Optimism
(range 4-20)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Treatment Specific Optimism by
Medical and Demographic Characteristics
22.4
22.7
23.0
21.7
23.2
22.7
22.3
22.3
22.8
20.0
20.5
21.0
21.5
22.0
22.5
23.0
23.5
24.0
24.5
25.0
Life Orientation Test
(range 14-30)
surgery
radiation
hormones
Age < 61
Age > 62
PSA < 5.8
PSA > 5.8
Gleason < 6
Gleason > 7
Dispositional Optimism by
Medical and Demographic Characteristics
Notes: Sample sizes for Treatment Modality are: Surgery (n=24), Radiation (n=18), and Hormones (n=11); for Age: 61 or younger (n=28) and 62 or older
(n=29); for PSA: 5.8 or lower (n=29) and 5.85 or higher (n-28); for Gleason Scores: 6 or less (n=39) and 7 or greater (n=18). Significant F-values for group
differences below alpha of 0.10 are indicated; alphas above the 0.10 cut-off are not listed.
p < 0.001
127
To examine how age and disease severity related to measures of the treatment
decision making process by treatment group, zero-order correlations were conducted
and listed in Table 20 to view each treatment group simultaneously. Patterns of
association were evident concerning how disease severity related to treatment
decision making measures across the treatment groups. For instance, among surgery
patients, PSA values were negatively related to treatment information
communication and satisfaction scores, indicating that higher PSA was related to less
overall treatment information communication and satisfaction with treatment choice
and decision making. But for radiation patients, higher PSA was related to greater
amounts of information received, yet trended toward less involvement in the decision
making process. Another association was evident in the data with respect to
treatment-specific optimism. These data suggested that among radiation patients,
greater disease severity was associated with less treatment-specific optimism, an
association not observed among surgery or hormone patients. Few associations were
evident for age across treatment groups, but the data did indicate that older age
among hormone patients trended toward receiving less information, whereas older
age among surgery patients was related to having better treatment understanding and
higher dispositional optimism, and among radiation patients, better appraisal of
physician communication.
128
Table 20
Correlations of Demographic/Medical Variables and Pretreatment Predictor and Post-Treatment Criterion Measures by
Treatment Modality
a
Surgery Radiation Hormone
n=24 n=18 n=11
Age Gleason PSA Age Gleason PSA Age Gleason PSA
Predictor Variables, Pretreatment
Treatment Information Communication
Communication with Medical
Team 0.255 -0.146 -0.553** -0.112 -0.246 0.536* -0.534
†
0.074 -0.178
Patient Perceived Understanding 0.418* 0.185 -0.444* 0.002 -0.424
†
0.017 0.074 -0.228 -0.077
PACE 0.314 -0.015 -0.548** 0.413
†
0.211 0.246 0.194 0.174 -0.170
Treatment Choice Deliberation
Factors Influencing Treatment
Choice 0.021 -0.463* 0.097 -0.363 -0.140 -0.235 -0.301 -0.530
†
-0.104
Involvement in Treatment
Decision Making 0.332 0.212 -0.143 -0.105 -0.265 -0.415
†
-0.229 -0.060 -0.509
Problem-Focused Coping -0.284 -0.473* 0.220 -0.166 0.105 -0.174 -0.370 -0.296 -0.640*
Treatment Choice Appraisal
Treatment Decision Making
Consonance -0.113 0.588** 0.265 0.034 0.113 0.188 0.451 0.134 -0.037
Satisfaction with Choice 0.072 0.199 -0.368
†
0.420
†
-0.063 0.213 0.033 -0.395 -0.083
Satisfaction with Decision
Making Involvement 0.183 -0.083 -0.505* -0.118 -0.138 -0.015 0.087 -0.151 -0.026
Treatment-Specific Optimism -0.167 -0.185 -0.107 0.215 -0.450
†
-0.438
†
0.263 -0.021 0.289
Life Orientation Test 0.447* 0.097 0.381
†
-0.128 -0.175 -0.382 -0.158 0.348 -0.074
129
Table 20 (continued)
Correlations of Demographic/Medical Variables and Pretreatment Predictor and Post-Treatment Criterion Measures by
Treatment Modality
a
Surgery Radiation Hormone
n=24 n=18 n=11
Age Gleason PSA Age Gleason PSA Age Gleason PSA
Criterion Variables, One-Month Post Treatment
General Quality of Life
FACT-G Summary:
General Well Being 0.329 0.283 -0.060 0.492* -0.178 -0.294 -0.006 0.078 -0.335
FACT Subscales:
Emotional Well Being 0.271 0.260 -0.168 0.615** -0.210 -0.280 0.264 0.219 -0.182
Functional Well Being 0.275 0.292 -0.049 0.393 -0.040 -0.367 -0.164 -0.287 -0.309
Physical Well Being 0.279 0.322 -0.194 0.294 -0.011 -0.124 -0.047 0.122 -0.358
Social Well Being 0.172 0.098 0.210 0.538* -0.293 -0.163 -0.019 0.086 -0.251
Prostate Cancer Quality of Life
FACT-P:
Prostate Cancer Well Being -0.039 0.016 0.335 -0.281 -0.067 -0.003 -0.222 0.339 -0.500
PCI: Bowel Functioning -0.301 0.095 0.319 0.309 -0.313 -0.444
†
-0.431 0.123 -0.173
Sexual Functioning -0.340 -0.071 0.283 -0.472* -0.113 -0.284 -0.540
†
-0.275 -0.436
Urinary Functioning -0.257 0.113 0.261 0.193 0.121 0.228 -0.116 0.378 -0.544
†
Psychosocial Adjustment
Stress: IES-R 0.025 -0.022 -0.025 0.411 0.536* 0.526* 0.239 0.623* 0.073
Mood: PANAS: Positive Mood 0.244 0.106 0.181 -0.115 0.088 -0.248 -0.392 -0.368 -0.486
Negative Mood 0.104 -0.274 -0.116 0.512* 0.369 0.541* 0.240 0.649* 0.169
Depression: CES-D -0.352
†
-0.406* -0.203 0.310 0.256 0.429
†
0.501 0.531
†
0.256
Notes: PACE=Patient Appraisal of Physician Communication Effectiveness; FACT=Functional Assessment of Cancer Therapy; FACT-G-Functional Assessment of
Cancer Therapy, General; FACT-P=Functional Assessment of Cancer Therapy, Prostate Specific; PCI=Prostate Cancer Index; IES-R = Impact of Events Scale;
PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression Scale.
a
For continuous variables, Pearson product-moment correlations were conducted. For dichotomous variables, Kendall's tau-b or Spearman's rho correlations
were conducted where applicable.
†
p<.10, *p < .05, **p<.01, two-tailed
130
These data may suggest that in some instances, age and disease severity may
interact with treatment modality specific to the treatment decision making process.
No study to date has tested how patient age or disease severity is related to treatment
decision making; nevertheless, it would not be unreasonable to assume that patients
of varying ages or having different prognoses would experience treatment decision
making in different ways. For instance, others have suggested that older patients –
especially older men –regard medicine as a paternalistic practice, an assumption used
to support why these patients neither prefer nor wish to be involved in treatment
decision making (Meyer, Russo, & Talbot, 1995; Petrisek, Laliberte, Allen, & Mor,
1997). However, the ways in which age and treatment severity together may interact
with patient participation in treatment decision making have yet to be examined.
With respect to disease severity, available prostate cancer treatment options are
based on several patient factors, one of these being disease prognosis (Pickett et al.,
2000). Therefore, the number of available treatment options may be a function of the
severity of one’s disease, and thus may dictate the extent to which patients are
involved in decision making. Given that the interactions between age or disease
severity with treatment decision making have not been addressed in the treatment
decision making literature nor in the current study, exploratory analyses are
warranted here to examine the relationships of the treatment decision making
processes between disease severity and age, and whether these are moderated by
treatment modality.
131
Interactions of Treatment Modality and Age
Exploratory hierarchical regressions were conducted in order to determine
whether the interactions of treatment modality and age predicted the processes of
treatment decision making. In the first block, the four variables comprising age and
treatment modality (i.e., having had surgery, radiation, or ADT) were entered, and
the second block contained their three centered cross-products. A total of nine
analyses were conducted each predicting the nine measures comprising the treatment
decision making process (please refer to Appendix A). The interactions between age
and treatment modality predicted only two outcomes. In one instance, treatment
modality was observed to moderate the relationship between age and patient
appraisal of physician communication effectiveness (Figure 6). Although there was
no significant main effect of treatment modality on patients’ appraisal of physician
communication effectiveness, there was a positive correlation between age and
patient appraisal of physician communication. This relationship was modified by
hormone therapy, indicating that older patients who did not receive hormone therapy
reported higher appraisal of their physicians’ communication effectiveness (partial r
= 0.34,Z = 0.53, SE = 0.23, t = 2.50, p=0.016).
In the other instance, treatment modality was observed to moderate age and
factors influencing treatment choice (Figure 7). The data revealed that although there
were no significant main effects of treatment modality or age on the number of
influential factors considered in making treatment choices, the relationship between
132
age and influential factors was modified by whether patients received surgery or
hormone therapy. The results indicated that younger patients who did not receive
surgery or hormone treatment considered more factors influential in making their
treatment choices (surgery x age: partial r = -0.40, Z = -0.45, SE = 0.15, t = -3.05,
p=0.004; and hormone x age: partial r = -0.38, Z = -0.35, SE = 0.12, t = -2.85,
p=0.006).
Interactions of Treatment Modality and Disease Severity
Exploratory hierarchical regressions were conducted in order to determine whether
the interactions of treatment modality and disease severity
8
predicted the processes
of treatment decision making. In the first block, the four variables comprising PSA
values and treatment modality (i.e., having had surgery, radiation, or ADT) were
entered, and the second block contained their three centered cross-products. A total
of nine analyses were conducted each predicting the nine measures comprising the
treatment decision making process. The interactions between PSA values and
treatment modality predicted only two outcomes. These interactions can be viewed
in Figures 8 and 9.
With respect to patients’ reported involvement in their treatment decision making,
there was no significant main effect of treatment type on involvement in

8
Results revealed that treatment modality did not interact with Gleason scores in predicting treatment
decision making; therefore the results will only discuss the findings concerning the interactions
between PSA values and treatment modality.
133
Figure 6
Interactions of Treatment Modality and Age on Patient Appraisal of Physician
Communication Effectiveness

10
13
16
19
22
25
28
31
34
Younger Age Older Age
Mean Appraisal of Physical Communication
Had Surgery
Did not have Surgery
10
13
16
19
22
25
28
31
34
Younger Age Older Age
Mean Appraisal of Physical Communication
Had Radiation
Did not have Radiation
10
13
16
19
22
25
28
31
34
Younger Age Older Age
Mean Appraisal of Physical Communication
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.124; F(4,52) = 2.97, p = 0.028; Step 2: F(7,49) = 2.71, p =
0.019. [R
2
= 0.093; [F = 2.11, p = 0.112; Hormone X Age interaction significant at p = 0.016.
Surgery X Age and Radiation X Age interactions not significant.

134
Figure 7
Interactions of Treatment Modality and Age on Factors Influencing Treatment
Choice

0
2
4
6
8
10
12
14
Younger Age Older Age
Mean Influences to Treatment Choice
Had Surgery
Did not have Surgery
0
2
4
6
8
10
12
14
Younger Age Older Age
Mean Influences to Treatment Choice
Had Radiation
Did not have Radiation
0
2
4
6
8
10
12
14
Younger Age Older Age
Mean Influences to Treatment Choice
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.046; F(4,52) = 0.167, p = 0.170; Step 2: F(7,49) = 2.62, p
= 0.022. [R
2
= 0.158; [F = 3.55, p = 0.021; Surgery X Age and Hormone X Age interactions
significant at p = 0.004 and p = 0.006, respectively. Radiation X Age interaction not significant.

135
treatment decision making, however PSA values were negatively correlated with
treatment decision making involvement (see Figure 8). This latter relationship was
modified by having of hormone therapy, indicating that ADT patients with higher
PSA were less likely to be involved in their treatment decision making than ADT
patients with lower PSA (partial r = 0.35, Z = 1.90, SE = 0.46, t = 2.59, p=0.013).
With respect to patients’ satisfaction with their treatment decision making
involvement (Figure 9), the data revealed that although there were no significant
main effects of treatment type or PSA values on patients’ satisfaction, the
relationship between satisfaction with involvement and PSA was modified by
whether patients had either surgery or hormone therapy to treat their cancer. The
results of the hierarchical regressions indicated that lower PSA values among surgery
and hormone patients were associated with greater satisfaction with decision making
involvement (surgery x PSA: partial r = 0.30, Z = 10.67, SE = 0.29, t = 2.22,
p=0.031; and hormone x PSA: partial r = 0.42, Z = 0.70, SE = 0.20, t = 3.21,
p=0.002).
Correlations of Treatment Decision Making, and Quality of Life by Treatment
Modality
The exploratory analyses discussed above considered how patients’
experience of the treatment decision making process may vary as a function of their
treatment modality, age, or disease severity. The next set of exploratory analyses,
which corresponded to the thrust of this current study, was to observe whether
patients’ post-treatment quality of life varied as a function of their treatment
136
Figure 8
Interactions of Treatment Modality and PSA on Involvement in Treatment Decision
Making

20
22
24
26
28
30
32
34
36
38
40
42
44
46
Low PSA High PSA
Mean Involvement in Decision Making
Had Surgery
Did not have Surgery
20
22
24
26
28
30
32
34
36
38
40
42
44
46
Low PSA High PSA
Mean Involvement in Decision Making
Had Radiation
Did not have Radiation
20
22
24
26
28
30
32
34
36
38
40
42
44
46
Low PSA High PSA
Mean Involvement in Decision Making
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.015; F(4,52) = 1.218, p = 0.315; Step 2: F(7,49) = 2.38, p = 0.036.
[R
2
= 0.168; [F = 3.67, p = 0.018; Hormone X PSA interaction significant at p = 0.013. Surgery X PSA
and Radiation X PSA interactions not significant.

137
Figure 9
Interactions of Treatment Modality and PSA on Satisfaction with Treatment
Decision Making Involvement

8
10
12
14
16
18
20
Low PSA High PSA
Mean Satisfaction with Decision Making
Had Surgery
Did not have Surgery
8
10
12
14
16
18
20
Low PSA High PSA
Mean Satisfaction with Decision Making
Had Radiation
Did not have Radiation
8
10
12
14
16
18
20
Low PSA High PSA
Mean Satisfaction with Decision Making
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.051; F(4,52) = 1.76, p = 0.152; Step 2: F(7,49) = 2.72, p = 0.018.
[R
2
= 0.161; [F = 3.65, p = 0.019; Surgery X PSA and Hormone X PSA interactions significant at p =
0.031 and p = 0.002, respectively. Radiation X PSA interaction not significant.
138
modality and their participation in treatment decision making. Tables 21a-c display
the results of the exploratory bivariate correlations between the measures of the
treatment decision making process and post-treatment health-related quality of life
for the patients grouped by each of the three treatment modalities.
Examination of these data revealed that correlations between pretreatment
treatment decision making processes and post-treatment quality of life yielded
potentially different patterns of associations for each of the three treatment
modalities. For instance, certain patterns were evident among surgery patients
(Table 21a) such that greater participation in the treatment decision making process
was related to better general quality of life, but worse prostate cancer quality of life
and mood. Another potential pattern was observed concerning treatment-specific
optimism. Greater optimism about treatment among surgery patients was correlated
with worse post-treatment general and sexual functioning, as well as greater negative
mood and depression. These exploratory correlational data of the surgery patients in
this study do not appear to support the widely held assumptions that post-treatment
quality of life outcomes are to benefit from patients’ participation in treatment
decision making.
However, among hormone patients (see Table 21c), the correlational data
revealed possible support for the potential benefits of patient participation in
treatment decision making and quality of life outcomes. In general, most measures of
the treatment decision process were positively related to post-treatment general
139
Table 21a
Correlations of Measures of the Treatment Decision Making process and One-Month Post-Treatment Health-Related
Quality of Life, among Prostatectomy patients (n=24)
Treatment Information
Communication Treatment Choice Deliberation Treatment Choice Appraisal
Post-Treatment Health-Related
Quality of Life CMT PPU PACE FITC ITDM P-F COPE SWC SWI TSO
General Quality of Life
FACT-G Summary:
General Well Being 0.255 0.352* 0.346* -0.124 0.096 -0.054 0.288
†
-0.089 -0.206
FACT Subscales:
Emotional Well Being 0.172 0.418* 0.158 0.006 0.455* 0.034 0.313
†
0.138 0.213
Functional Well Being 0.284† 0.272
†
0.245 -0.135 -0.157 -0.200 -0.240 -0.037 -0.314
†
Physical Well Being 0.339
†
0.461* 0.498** -0.288
†
-0.007 -0.252 0.384* 0.248 -0.201
Social Well Being 0.044 -0.130 0.120 0.123 0.178 0.223 -0.032 -0.117 -0.246
Prostate Cancer Quality of Life
FACT-P:
Prostate Cancer Well Being -0.091 -0.167 -0.067 0.191 -0.276
†
0.222 -0.217 -0.343
†
-0.231
PCI: Bowel Functioning -0.410* -0.159 -0.239 -0.181 -0.329
†
-0.046 0.020 -0.347* -0.093
Sexual Functioning -0.447* -0.462* -0.692*** 0.238 -0.391* 0.334† -0.178 -0.475** -0.298
†
Urinary Functioning -0.398* 0.053 -0.218 -0.189 -0.233 0.147 -0.076 -0.292
†
-0.066
Psychosocial Adjustment
Stress: IES-R -0.126 0.220 0.060 0.094 0.125 -0.033 0.075 0.068 0.062
Mood: PANAS: Positive Mood 0.304
†
-0.052 0.154 0.202 0.084 0.146 -0.232 -0.048 0.019
Negative Mood 0.001 0.289
†
0.151 -0.016 0.527** 0.176 0.051 0.348
†
0.371*
Depression: CES-D -0.148 0.025 0.038 0.079 0.022 0.266 0.023 0.112 0.427*
Notes: CMT=Communication with Medical Team; PPU=Patient Perceived Understanding; PACE=Patient Appraisal of Physician Communication Effectiveness;
FITC=Influences to Treatment Decision Making; ITDM=Treatment Decision Making Involvement; P-F COPE=Problem-Focused Coping; SWC=Satisfied with
Treatment Choice; SWI=Satisfied with Decision Making Involvement; TSO=Treatment-Specific Optimism; FACT=Functional Assessment of Cancer Therapy;
FACT-G=Functional Assessment of Cancer Therapy, General; FACT-P=Functional Assessment of Cancer Therapy, Prostate Specific; PCI=Prostate Cancer Index;
IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression Scale.
†
p<.10, *p < .05, **p<.01, ***p<.001, two-tailed
140
Table 21b
Correlations of Measures of the Treatment Decision Making process and One-Month Post-Treatment Health-Related
Quality of Life, among Radiation patients (n=18)
Treatment Information
Communication Treatment Choice Deliberation Treatment Choice Appraisal
Post-Treatment Health-Related CMT PPU PACE FITC ITDM P-F COPE SWC SWI TSO
Quality of Life
General Quality of Life
FACT-G Summary:
General Well Being 0.303 -0.244 0.160 0.056 0.277 -0.063 0.108 0.055 0.232
FACT Subscales:
Emotional Well Being 0.293 -0.196 0.293 -0.250 0.146 -0.106 0.387
†
-0.038 0.356
†
Functional Well Being 0.419* -0.376* -0.043 0.030 0.402* 0.034 0.027 -0.201 -0.004
Physical Well Being 0.014 -0.095 0.098 0.171 -0.129 -0.370
†
-0.148 -0.09 -0.003
Social Well Being 0.079 -0.054 0.143 -0.105 0.280 0.034 0.144 -0.066 0.398
†
Prostate Cancer Quality of Life
FACT-P: Prostate Cancer Well Being 0.200 -0.025 -0.117 0.060 0.027 0.063 0.055 0.481* -0.296
PCI: Bowel Functioning 0.126 -0.044 0.301 -0.184 -0.169 -0.357/ 0.340
†
-0.990 0.351
†
Sexual Functioning 0.317 -0.011 -0.093 0.375/ 0.152 0.069 -0.386
†
0.125 -0.180
Urinary Functioning 0.012 0.001 0.419* -0.241 -0.328
†
-0.075 0.362
†
0.000 0.212
Psychosocial Adjustment
Stress: IES-R -0.218 -0.384
†
0.233 -0.409* -0.213 0.030 0.218 0.093 -0.233
Mood: PANAS: Positive Mood 0.171 -0.023 -0.208 0.569** -0.068 -0.208 -0.507* -0.443* -0.194
Negative Mood -0.164 -0.179 0.244 -0.410* -0.180 -0.029 0.391
†
0.025 -0.125
Depression: CES-D -0.184 -0.152 0.292 -0.368
†
0.081 0.132 0.424* 0.235 0.059
Notes: CMT=Communication with Medical Team; PPU=Patient Perceived Understanding; PACE=Patient Appraisal of Physician Communication Effectiveness;
FITC=Influences to Treatment Decision Making; ITDM=Treatment Decision Making Involvement; P-F COPE=Problem-Focused Coping; SWC=Satisfied
with Treatment Choice; SWI=Satisfied with Decision Making Involvement; TSO=Treatment-Specific Optimism; FACT=Functional Assessment of Cancer
Therapy; FACT-G=Functional Assessment of Cancer Therapy, General; FACT-P=Functional Assessment of Cancer Therapy, Prostate Specific; PCI=
Prostate Cancer Index; IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies,
Depression Scale.
†
p<.10, *p < .05, **p<.01, ***p<.001, two-tailed
141
Table 21c
Correlations of Measures of the Treatment Decision Making process and One-Month Post-Treatment Health-Related
Quality of Life, among Hormone patients (n=11)
Treatment Information
Communication Treatment Choice Deliberation Treatment Choice Appraisal
Post-Treatment Health-Related
Quality of Life CMT PPU PACE FITC ITDM
P-F
COPE SWC SWI TSO
General Quality of Life
FACT-G Summary:
General Well Being 0.482
†
0.488
†
0.555* -0.419 0.563* 0.490/ 0.610* -0.085 -0.213
FACT Subscales:
Emotional Well Being 0.313 0.301 0.550* -0.364 0.361 0.216 0.561* -0.147 0.054
Functional Well Being 0.733** 0.603* 0.611* -0.314 0.699** 0.613* 0.517
†
0.200 -0.463
†
Physical Well Being 0.487
†
0.240 0.621* -0.467
†
0.727** 0.527* 0.544* 0.294 -0.414
Social Well Being 0.088 0.503/ 0.046 -0.042 0.130 0.364 0.417 -0.350 0.148
Prostate Cancer Quality of Life
FACT-P: Prostate Cancer Well Being 0.396 -0.044 0.351 -0.383 0.431
†
0.302 0.179 -0.089 -0.073
PCI: Bowel Functioning 0.795** 0.246 0.322 -0.220 0.351 0.316 0.429
†
-0.045 -0.118
Sexual Functioning 0.096 0.087 -0.268 0.581* 0.199 0.566* 0.294 -0.258 0.131
Urinary Functioning 0.209 -0.206 0.246 -0.390 0.227 0.324 0.065 -0.327 -0.074
Psychosocial Adjustment
Stress: IES-R 0.255 -0.292 0.400 -0.753** -0.049 0.113 -0.322 -0.135 -0.282
Mood: PANAS: Positive Mood 0.351 0.470
†
0.111 0.185 0.487
†
0.520* 0.051 0.236 -0.433
†
Negative Mood -0.143 -0.767** 0.002 -0.524* -0.386 -0.551* -0.573* -0.246 0.099
Depression: CES-D -0.265 -0.555* 0.085 -0.501
†
-0.393 -0.510
†
-0.244 -0.250 0.283
Notes:
CMT=Communication with Medical Team; PPU=Patient Perceived Understanding; PACE=Patient Appraisal of Physician Communication Effectiveness;
FITC=Influences to Treatment Decision Making; ITDM=Treatment Decision Making Involvement; P-F COPE=Problem-Focused Coping; SWC=Satisfied with
Treatment Choice; SWI=Satisfied with Decision Making Involvement; TSO=Treatment-Specific Optimism; FACT=Functional Assessment of Cancer Therapy;
FACT-G=Functional Assessment of Cancer Therapy, General; FACT-P=Functional Assessment of Cancer Therapy, Prostate Specific; PCI=Prostate Cancer
Index; IES-R=Impact of Events Scale; PANAS=Positive and Negative Affect Scale; CES-D=Centers for Epidemiological Studies, Depression Scale.
†
p<.10, *p < .05, **p<.01, ***p<.001, two-tailed
142
quality of life and psychosocial outcomes for hormone patients, indicating that
greater participation in treatment decision making was related to better overall
quality of life, prostate cancer quality of life, and mood. The greater the number of
factors hormone patients reported as being influential during decision making was
correlated with better sexual functioning, and less stress, negative mood and
depression, but worse post-treatment physical well-being. Higher treatment-specific
optimism among hormone patients was associated with worse physical well-being
and lower positive mood.
Radiation patients’ experience with the treatment decision making process
related to few post-treatment quality of life outcomes (Table 21b). However, similar
to the hormone patients, the greater number of factors that radiation patients reported
as being influential during decision making was related to better one-month post-
treatment sexual functioning and positive mood, and less stress, negative mood, and
depression. Trends in treatment-specific optimism among radiation patients were
observed such that higher treatment-specific optimism was related to better
emotional and social well-being, and better bowel functioning at one-month after
treatment.
By examining the relationships between measures of the treatment decision
making process and post-treatment quality of life for each of the treatment modalities
offers an unique consideration of these relationships that otherwise would have been
overlooked when reviewing the correlational data of all subjects grouped together
143
(for review, please refer back to Tables 11,13, and 16). As a result, certain variables,
such as involvement in treatment decision making and treatment-specific optimism,
surfaced as being either negatively or positively related to quality of life outcomes
when patients were considered separately by treatment groups. For instance, greater
involvement in treatment decision making among hormone patients trended toward
better prostate cancer quality of life, whereas the opposite was evident among
surgery patients (see Tables 21c and 21a, respectively). Given how the current
medical lore advocates that patient involvement in treatment decision making is
beneficial to post-treatment quality of life (Davison & Goldenberg, 2002; Hack et al.,
2006), the data here suggested that greater involvement among surgery patients
could be possibly harmful. Such a possibility warrants further examination to
determine the nature of these interactions. Therefore, involvement in treatment
decision making and treatment-specific optimism were further examined in order to
determine whether they interacted with treatment modality in predicting patient post-
treatment health-related quality of life.
Interactions of Involvement in Decision Making and Treatment Modality
Exploratory hierarchical regressions were conducted in order to determine
whether the interactions of treatment modality and patient involvement with
treatment decision predicted post-treatment quality of life. In the first block, the four
variables comprising patient involvement with treatment decision making (TDM)
and treatment modality (i.e., having had surgery, radiation, or ADT) were entered,
144
and the second block contained their three centered cross-products. A total of
thirteen analyses were conducted each predicting the thirteen measures comprising
health-related quality of life (please refer to Appendix A). Figures 10 through 13
illustrate the findings from the four significant moderated relationships.
The data demonstrated that a few possible interactions may exist. With
respect to patients’ reported one-month functional well-being, as measured by the
FACT Functional subscale, there was no significant main effect of treatment
modality on this quality of life outcome (see Figure 10). However involvement in
treatment decision making and functional well-being were positively correlated and
this relationship was further modified by treatment modality such that less
involvement in decision making among surgery patients was related to better
functional well-being at one-month (partial r = 0.31, Z = 0.89, SE = 0.39, t = 2.25,
p=0.029). Post-treatment physical well-being, as measured by the FACT Physical
subscale (Figure 11), also was predicted by the interaction terms. Although there was
no significant main effect of involvement in treatment decision making on physical
well-being, this relationship was modified by having received radiation treatment
such that the less involved radiation patients were in their decision making, the better
their physical well-being at one-month (partial r = 0.28, Z = 0.44, SE = 0.35, t = 2.07,
p=0.044). However, the data revealed the opposite for hormone patients. The
relationship of involvement in treatment decision making also was modified by
having received hormone therapy such that the less involved these patients were in
145
their decision making, the worse their post-treatment physical well-being (partial r =
-0.34, Z = -0.56, SE = 0.33, t = -2.56, p=0.014).
In terms of prostate cancer quality of life post-treatment, Figure 12 illustrates
these data. There were no significant main effects of involvement in treatment
decision making and treatment modality on prostate cancer well-being, as measured
by the FACT-P, however interactions between involvement in decision making and
treatment modality uniquely accounted for the variance in post-treatment prostate
cancer well-being ([F(7,49) = 3.06, p=0.037). These data indicated that the less
involved surgery and radiation patients were in their treatment decision making, the
better their reported prostate cancer quality of life at one-month post-treatment
(surgery x TDM: partial r = 0.34, Z = 1.62, SE = 0.64, t = 2.53, p=0.015; and
radiation x TDM: partial r = 0.32, Z = 1.22, SE = 0.52, t = 2.34, p=0.023). In terms
of post-treatment negative mood, as measured by the PANAS (see Figure 13), there
were no significant main effects of treatment modality or involvement in treatment
decision making on negative mood; however, interactions between involvement in
decision making and treatment modality surfaced to uniquely account for negative
mood ([F(7,49) = 6.71, p=0.001). The data indicated that the less involved surgery
or radiation patients were in their decision making, the less negative mood they
reported at one-month post treatment (surgery x TDM: partial r = -0.54, Z = -2.06,
SE = 0.46, t = -4.29, p<0.001; and radiation x TDM: partial r = -0.34, Z = -1.04, SE =
0.37, t = -2.48, p=0.014).
146
Figure 10
Interactions of Treatment Modality and Involvement in Treatment Decision Making
(TDM) on Functional Well-Being

1
3
5
7
9
11
13
15
17
19
21
23
25
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Functional Well-being
Had Surgery
Did not have Surgery
1
3
5
7
9
11
13
15
17
19
21
23
25
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Functional Well-being
Had Radiation
Did not have Radiation
1
3
5
7
9
11
13
15
17
19
21
23
25
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Functional Well-being
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.026; F(4,52) = 1.37, p = 0.256; Step 2: F(7,49) = 2.88, p = 0.064. [R
2
= 0.133; [F = 2.82, p = 0.048; Surgery X TDM interaction significant at p = 0.029. Radiation X TDM and
Hormone X TDM interactions not significant.
147
Figure 11
Interactions of Treatment Modality and Involvement in Treatment Decision Making
(TDM) on Physical Well-Being

15
17
19
21
23
25
27
29
31
33
35
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Physical Well-being
Had Surgery
Did not have Surgery
15
17
19
21
23
25
27
29
31
33
35
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Physical Well-being
Had Radiation
Did not have Radiation
15
17
19
21
23
25
27
29
31
33
35
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Physical Well-being
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.009; F(4,52) = 1.13, p = 0.352; Step 2: F(7,49) = 1.81, p = 0.106.
[R
2
= 0.126; [F = 2.58, p = 0.064; Radiation X TDM and Hormone X TDM interactions significant at
p = 0.044 and p = 0.014, respectively. Surgery X TDM interaction not significant.

148
Figure 12
Interactions of Treatment Modality and Involvement in Treatment Decision Making
(TDM) on Prostate Cancer Well-Being

15
17
19
21
23
25
27
29
31
33
35
37
39
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Prostate Cancer Well-being
Had Surgery
Did not have Surgery
15
17
19
21
23
25
27
29
31
33
35
37
39
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Prostate Cancer Well-being
Had Radiation
Did not have Radiation
15
17
19
21
23
25
27
29
31
33
35
37
39
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Prostate Cancer Well-being
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.003; F(4,52) = 1.05, p = 0.393; Step 2: F(7,49) = 1.98, p = 0.077.
[R
2
= 0.146; [F = 3.06, p = 0.037; Surgery X TDM and Radiation X TDM interactions significant at p=
0.015 and p = 0.023, respectively. Hormone X TDM interaction not significant.
149
Although these findings are of an exploratory nature and are thus tentative,
they may suggest that in some instances, not all patients’ general quality of life at
one month after treatment benefits from their active involvement treatment decision
making, but that less involvement in decision making may yield better quality of life.
Such findings could serve to drive future investigation into this matter.
Interactions of Treatment-Specific Optimism and Treatment Modality
The last series of post-hoc regressions was conducted to evaluate whether
patient post-treatment quality of life was a function of treatment modality and
treatment-specific optimism. The exploratory correlational data suggested that
treatment-specific optimism may be related to some positive quality of life outcomes
for radiation patients, but not for hormone or surgery patients (see Tables 21a-c).
Given how others have suggested that treatment-specific optimism may
buffer against post-treatment distress and depression among colon or breast cancer
patients (Cohen et al., 2001; Stanton et al., 1998), determining whether treatment-
specific optimism interacted with treatment modality to predict health-related quality
of life is warranted.
Therefore, exploratory hierarchical regressions were conducted in order to
determine whether the interactions of treatment modality and treatment-specific
optimism predicted post-treatment quality of life. In the first block, the four variables
comprising treatment-specific optimism and treatment modality (i.e., having had

150
Figure 13
Interactions of Treatment Modality and Involvement in Treatment Decision Making
(TDM) on Negative Mood

6
8
10
12
14
16
18
20
22
24
26
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Negative Mood
Had Surgery
Did not have Surgery
6
8
10
12
14
16
18
20
22
24
26
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Negative Mood
Had Radiation
Did not have Radiation
6
8
10
12
14
16
18
20
22
24
26
Less Involvement in
TDM
Greater Involvement in
TDM
Mean Negative Mood
Had Hormones
Did not have Hormones
Notes: Overall Model, Step 1: Adj R
2
= 0.069; F(4,52) = 0.096, p = 0.983; Step 2: F(7,49) = 2.95, p
= 0.012. [R
2
= 0.289; [F = 6.71, p = 0.001; Surgery X TDM and Radiation X TDM interactions significant
at p < 0.001 and p = 0.007, respectively. Hormone X TDM interaction not significant.

151
surgery, radiation, or ADT) were entered, and the second block contained their three
centered cross-products. A total of thirteen analyses were conducted each predicting
the thirteen measures comprising health-related quality of life. The results of these
analyses, however, revealed that the interaction terms of treatment modality and
treatment-specific optimism did not predict any post-treatment quality of life
measure.
Discussion
In this study we conducted a comprehensive assessment of the treatment
decision making process and health-related quality of life in a sample of men who
had been diagnosed with prostate cancer, treated, and assessed at pretreatment and at
one-month after primary treatment. Our goals were to provide a description of
prostate cancer patients’ experience in the treatment decision making process and to
identify which aspects (if any) of the treatment decision making process uniquely
related to changes in post-treatment health-related quality of life. The treatment
decision making process was conceived in this study as a multifaceted event
comprising aspects of treatment information communication, treatment choice
deliberation, and treatment choice appraisal. Each of these three aspects was
hypothesized to make unique contributions to patients’ post-treatment quality of life
adjustment; hypotheses and questions were tested in this regard.
In order to evaluate the treatment decision making process, several measures
were developed based on decision making theory and principles. This undertaking
152
was necessary given the absence of theory-driven research concerning patient
treatment decision making participation. The results of the factor analyses indicated
that most study-designed scales were generally adequate as measures representing
the treatment decision making process, such that the measures possessed good factor
structures and internal reliability. Furthermore, the results from the intercorrelational
analyses indicated that most measures from the three components – treatment
information communication, choice deliberation, and choice appraisal – related to
each other in ways to indicate the possible presence of construct validity. Although
this study was not envisioned as a validation study, the data signify that the study-
designed scales have potential as meaningful measurements which could benefit
from future validity analyses. The conceptualization of the treatment decision
making process in this study suggested that the treatment decision making process is
a multifaceted event and demonstrated that the measurements used were a useful
strategy for assessing treatment decision making among cancer patients.
Description of Patients’ Health-Related Quality of Life
At both time points the prostate cancer patients in this study reported having
good health-related quality of life. These findings are in agreement with findings
from previous research (Penson et al., 2003; Perez et al., 1997; Litwin et al., 1995).
The inclusion of a multidimensional, standardized measure of quality of life in this
study allowed for confirmation of this description through reference to both healthy
and prostate cancer normative samples. In some instances, the prostate cancer
153
patients in this study reported having better post-treatment prostate cancer quality of
life in comparison to others (Jenkins, Schover, Fouladi, Warneke, & Neese, 2004;
Jayadevappa et al., 2005). In terms of psychosocial adjustment, the patients in this
study reported levels of mood, depression, and stress comparable to findings in other
prostate cancer patient populations (Blank & Bellizzi, 2006; Joseph et al., 2006) and
in healthy age-matched populations (Lewinsohn, Seeley, Allen, & Roberts, 1997).
Although the men reported having good quality of life at both time points,
emotional well-being and mood were observed to improve at one-month after
primary treatment, whereas physical well-being and prostate cancer quality of life
were observed to decline at the one-month post-treatment time point. This latter
finding is not surprising given that it is expected for many prostate cancer patients to
still be experiencing treatment-related side effects at one month after the completion
of their primary treatment (O’Rourke, 2001). Also consistent with the prostate cancer
literature, study patients’ prostate cancer quality of life was correlated with treatment
modality, indicating that having had prostatectomy was related to worse sexual and
urinary functioning (O’Rourke, 2001; Litwin et al., 1998). Despite their decline in
prostate cancer well being, only a third of the men reported urinary trouble to be
bothersome, whereas approximately half the men reported being bothered by bowel
and sexual functioning troubles at one-month post-treatment. In spite of some of
their reported physical symptomological difficulties, the participants in this study
reported having generally good quality of life.
154
Description of Patients’ Treatment Decision Making Process
The results of the current study indicated that most patients had favorable
experiences of the treatment decision making process. Over 80% of the men reported
to have received ample treatment information from their medical team, had reported
understanding treatment information, and had rated their doctors above average as
effective communicators. These findings are compatible with other research
regarding prostate cancer patients’ experiences in receiving and discussing treatment
information (Davison et al., 2002; Deber et al., 1996; Lang, Floyd, & Beine, 2000,
Wong et al., 2000). However, the results from the exploratory analyses indicated that
possible interactions with medical and demographic factors could exist. For instance,
patient age was positively related to patient appraisal of physician communication
effectiveness, whereas the relationship between sicker surgery patients and lower
appraisal of physician communication was observed. Although the literature suggests
that most patients want to receive and discuss treatment information, few studies to
date has specifically addressed how patients’ experience with treatment information
communication may be a function of their age, treatment choice, or disease severity.
For instance, age and disease severity may be more than just factors of which to
consider when making the best treatment choice (Pickett et al., 2000), but also may
contribute to how patients actually experience their treatment information
communication. The exploratory findings from the current study are suggestive of
this possibility.
155
With respect to treatment choice deliberation, the majority of the men
reported being actively involved in making their treatment decisions; specifically,
nearly all patients (96%) had reported moderate-or-more involvement in decision
making. With respect to the number of factors considered influential during
treatment decision making, the data suggested that most men were influenced by
more than half of the 13 factors listed as influential to treatment choice. The most
influential factors reported were physician opinion, post-treatment functioning, and
family opinion, and these factors are ordered in importance comparable to those
identified by others (Holmboe & Concato, 2000; Zeliadt et al., 2006). Patient
characteristics also appeared to have some bearing on the choice deliberation
process. Disease severity was observed to be negatively related to involvement in
treatment decision making among ADT patients, as well as with the number of
factors considered influential in decision making. These exploratory findings lend
support to others who have suggested that patient involvement in decision making is
not embraced by all patients, especially those who may be sicker (Blanchard,
Labrecque, Ruckdeschel & Blanchard, 1988; Knight et al., 2003). Furthermore,
patients with greater disease severity may have fewer treatment options (De Mulder,
Schalken, & Sternberg, 2002) and as a result, may have fewer relevant factors in
which to consider while making their treatment decisions. These preliminary data,
suggesting that the manner in which patients’ prognoses, coupled with their choice of
treatment, could influence their involvement in decision making, may be a worthy
156
endeavor to investigate in order to provide physicians with better understanding of
what to expect from their patients.
With respect to treatment choice appraisal, two-thirds of the men in this study
reported that they were consonant in their treatment decision making, such that they
participated to the extent that they wished. However, of the one-third of the men who
were dissonant, 85% of them indicated they had more control over decision making
than they had preferred. Although this finding may be minor, it may exemplify
clinical importance concerning a subset of patients who are expected to actively
participate in treatment decision making and wish not to.
The data also indicated that over 75% of the men were satisfied with their
treatment choices as well as with their involvement in treatment decision making.
Exploratory data indicated that disease severity was negatively related to satisfaction
with decision making among surgery or ADT patients. It is not surprising to observe
that disease severity was related to satisfaction with treatment decision making.
Sicker patients may not have many treatment options from which to choose (Loblaw
et al., 2004) nor may be interested or willing to participate in making their treatment
decisions (Knight et al., 2003). Such conditions could be reflective of how the sicker
patients in this study reported satisfaction with their “treatment decision making.”
However, given the importance that others have stressed concerning the issue of
patient satisfaction with treatment decision making (Rimer et al., 2004; Sheriff et al.,
2005), medical personnel should be sensitive to their patients’ disease severity and
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decision making preferences when involving them during the treatment decision
making process.
In terms of treatment-specific optimism of the patients, the general findings
indicated that 79% of all patients were moderately-or-more optimistic of their chosen
treatment. However, ADT patients had significantly lower treatment-specific
optimism in comparison to both surgery and radiation patients. This is not surprising
given that both surgery and radiation treatments are often regarded as those which
“go for cure” (Kunkel et al., 2000; O’Rourke, 2001), where ADT is not; thus
hormone patients may not hold the same expectations for their treatment as would
surgery or radiation patients.
Treatment Decision Making Predicting Quality of Life
Two questions and three hypotheses were tested in this study. These
assertions were based on the existing literature which assumes that patient
participation in the treatment decision making process has positive impact on patient
post-treatment quality of life. All three aspects of the treatment decision making
process (i.e., treatment information communication, choice deliberation, and
treatment choice appraisal) were expected to contribute to the improvement in
patient health-related quality of life from pretreatment to one-month post-treatment.
In general, the results from the hierarchical regressions conducted found that
treatment information communication, treatment choice deliberation, and treatment
choice appraisal were, in essence, poor predictors of changes in post-treatment
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quality of life. In most instances, the best predictor of patient post-treatment quality
of life was patient pretreatment quality of life. This finding is no surprise given its
indication in similar prospective research (Cohen et al., 2001; Schou, Ekeberg,
Sandvik, Hjermstad, & Ruland, 2005; Stanton et al., 1998). However, a summary of
the data are worth further discussion and will be organized by the three components
of treatment decision making.
Treatment Information Communication
Measures of treatment information communication were examined to address
how they would relate to patient health-related quality of life prior to and after
treatment. These measures also were tested to observe whether they uniquely or
collectively contributed to the change in patient health-related quality of life at one
month post-treatment. Correlational findings indicated that treatment information
communication was related to more post-treatment quality of life measures than to
pretreatment measures, and the data indicated that greater treatment information
communication was related to better quality of life. These observations may be in
line with assumptions in the literature that information given and effectively
discussed at pretreatment serves to promote post-treatment quality of life (Gattellari
et al., 2001; Liang et al., 2002; Sainio et al., 2001; Stewart, 1995). However, the
predictive models demonstrated that measures of treatment information
communication did not account for changes to post-treatment quality of life. Having
controlled for confounds, where others have not (Joseph et al., 2006; Wong et al.,
159
2000), allowed us to observe that treatment information communication may not
have the unique aspects thought to be protective of post-treatment outcomes.
The series of exploratory analyses conducted post-hoc to observe whether
patients experienced the treatment information communication process as a function
of their treatment modality revealed that prostate cancer quality of life was
negatively associated with greater treatment information communication among
surgery patients. These data may suggest that greater amounts of information
provided to surgery patients could have a deleterious effect on patient prostate cancer
quality of life at post-treatment which is in direct contrast with the literature on
treatment information. However, this finding may be due to the fact that post-
treatment measures in this study were acquired at only one-month following primary
treatment, when prostate-related functioning is known to be worse for surgical
patients in comparison to other treatments (O’Rourke, 2001). Furthermore, surgery
patients may be “primed” to be aware of their physical symptomatology based on
receiving pretreatment information concerning expected treatment side effects that
immediately follow surgery (Wilkins, 1976). To be certain of the effects that
treatment information communication may have on patients receiving different
treatment modalities, quality of life data should be assessed either for change in
quality of life (as approached in this study) or for overall quality of life (as conducted
in the exploratory analyses), and be analyzed along a trajectory of post-treatment
time points, especially those points in time when varying treatment side effects are
160
thought to even out (Litwin et al., 1995). These slight but meaningful ways to assess
quality of life outcomes are important to note when considering whether treatment
information communication accounts for changes to quality of life, or contributes to
absolute quality of life.
Treatment Choice Deliberation
It was predicted that prostate cancer patients who were actively involved in
their treatment choice deliberation process would report better post-treatment health-
related quality of life than those patients who were not actively involved in this
process. It also was predicted that treatment choice deliberation would contribute
uniquely to the change in health-related quality of life over time, above and beyond
that of pretreatment treatment information communication and patient problem-
focused coping strategies. These predictions were partially supported by the data.
After controlling for pretreatment variables, the measures of treatment choice
deliberation were poor predictors of post-treatment quality of life outcomes with the
exception of prostate cancer well-being and urinary functioning. In both equations
did greater amounts of choice deliberation negatively account for these two
outcomes, indicating that the more involved patients were in choice deliberation, the
worse their prostate cancer quality of life. These findings are in direct contrast to
assumptions in the literature which advocates that patient involvement in treatment
decision making may be protective against poor quality of life adjustment (Frosch &
Kaplan, 1999; Hack et al., 2006). Two possible questions surfaced as a result of
161
these findings: (1) could treatment choice (the resulting outcome of choice
deliberation) have moderated the relationship between choice deliberation and
prostate cancer quality of life and thus more likely to have produced the negative
outcomes? Or (2) could a third variable, such as stress, be related to the choice
deliberation process in such a way as to account for the negative outcomes in
prostate cancer quality of life? Both of these questions were addressed through post-
hoc analyses.
In an attempt to decipher if patients’ treatment modality interacted with their
involvement in decision making in ways that would affect their post-treatment
quality of life, a series of post-hoc regressions was conducted. The findings indicated
that for surgery and radiation patients, the less involved they were in their decision
making, the better their post-treatment quality of life on several domains.
Interestingly though, ADT patients reported better physical functioning at post-
treatment when they were more involved in their decision making. Although not
theory-driven, these post-hoc data may indicate that patients’ involvement in their
decision making may, in fact, vary to some degree as a function of their treatment
modality. Clearly, additional exploration into this issue is warranted, especially in
regards to the unfavorable quality of life outcomes observed here.
Because it is unlikely that the extent to which one is involved in choice
deliberation has a direct effect on one’s post-treatment physical symptomatology, the
negligible findings from the original regression analyses may be better explained by
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the presence of another intervening factor. One of the more recent concerns
addressed in the treatment decision making literature is that of patients being
overwhelmed during the pretreatment process (Luce, 2005). Patient stress can have
harmful effects on well-being at any point during the cancer trajectory (Roth &
Cohen, 1986), and others have demonstrated that when stress is mediated by coping
behaviors, patients’ post-treatment adjustment is improved (Hamarat et al., 2001). To
date, no study has addressed how patient involvement in treatment decision making
may affect one’s pretreatment stress levels (i.e., would too much involvement reduce
or increase stress?). If there is a likelihood that treatment decision making interacts
with patient stress, then having not accounted for pretreatment stress in the current
study may have been a likely “third variable” affecting patient post-treatment quality
of life. Therefore, a series of post-hoc analyses was conducted to address this issue.
Post-hoc bivariate correlations were conducted to observe the relationships
among pre- and post-treatment stress (as measured by the IES-Summary scale),
health-related quality of life, and measures of the choice deliberation process. In
addition, two exploratory hierarchical regression analyses were conducted to test
whether (1) choice deliberation mediated the relationship between pretreatment
stress and post-treatment prostate-cancer quality of life and (2) pretreatment stress
interacted with the choice deliberation process predicting post-treatment prostate
cancer quality of life.
163
The exploratory correlations revealed that pretreatment and post-treatment
stress were negatively related to post-treatment quality of life outcomes, but
interestingly, did not relate to any of the treatment choice deliberation measures. As
for whether the process of choice deliberation served to mediate stress, neither the
summary scale of stress, nor its three subscales (i.e., intrusion, avoidance, and
hyperarousal) predicted either involvement in treatment decision making or the
number of influential factors considered in making treatment choice; therefore, no
relationship possibly could be mediated (Baron & Kenny, 1986). As for whether
stress and choice deliberation were better explained as one of moderation, none of
these hierarchical regression analyses revealed choice deliberation moderating
pretreatment stress. In one occasion did the interaction term of influential factors to
treatment choice and pretreatment stress account for an additional 6% of the variance
in prostate cancer quality of life, suggesting that greater stress coupled with
considering more factors predicted worse prostate cancer well-being. However, this
finding was not strong (Z = -2.17, SE = 0.11, t = -1.99, p=0.051).
Although these post-hoc attempts at interpreting these findings failed to
provide a clear explanation, one thing remains: Some factor(s) interacting with
patient choice deliberation may affect adjustment to patient post-treatment quality of
life. Active involvement in choice deliberation could engender feelings of
responsibility for treatment outcomes (Steginga & Occhipinti, 2002) or a sense of
commitment to self-care (Costanzo, 2006; Mullens, McCaul, Erickson, & Sandgren,
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2004; Sharpe, Butow, Smith, McConnell, & Clarke, 2005) that later have negative
effects on patient quality of life adjustment. Such possibilities certainly warrant
further research to address these very issues.
Treatment Choice Appraisal
It was predicted that prostate cancer patients who engaged in the treatment
decision making process consonant with their own preference would be more
satisfied with their treatment choice before and after primary treatment than would
patients whose actual involvement was disparate from their treatment decision
making preferences. The data initially did not support these expectations. Because
more than a 75% of the men in this study reported having been consonant in their
treatment decision making, the findings may be more likely due to the result of
skewed data than to any theoretical misconstruction. To retest the hypothesis, the
scale measuring satisfaction with involvement was observed to address similar
theoretical underpinnings suggested to define treatment decision making consonance
(Deber et al., 1996; Fallowfield, 2001). When this scale was substituted for treatment
decision making consonance in analyses, satisfaction with treatment decision making
involvement predicted satisfaction with treatment choice at both time points. This
secondary finding is in agreement with other studies (Gattellari et al., 2001; Lantz et
al., 2005), further supporting the importance of tailoring treatment decision making
participation to the needs of each individual patient that has been espoused by others
(Davison & Degner, 1997; Ganz, 2002; Hoffman et al., 2003; Kaplan, 1991).
165
It also was predicted that relationships between treatment-related optimism,
problem-focused coping, and health-related quality of life would be found. It was
expected that prostate cancer patients who were optimistic about their treatment
choice before having their treatment would report greater improvement in their
health-related quality of life post-treatment than patients who were less optimistic
about their treatment choice. It was predicted that these relationships between
treatment-specific optimism and quality of life would be mediated by coping; this
prediction was based on the accepted notion that problem-focused coping mediates
dispositional optimism, which then accounts for positive post-treatment functioning.
Thus it was anticipated that if treatment-specific optimism was related to
dispositional optimism, then treatment-specific optimistic patients would be more
likely to use problem-focused coping strategies leading to improved post-treatment
health-related quality of life, as indicated by the literature.
The data from this study partially supported these expectations. First,
treatment-specific optimism did not relate to problem-focused coping or to
dispositional optimism. These findings may indicate that treatment-specific optimism
is a distinct construct from that of dispositional optimism, as well as one that may
not interact with problem-focused coping as dispositional optimism has been
proposed to do (Carver et al., 1993). Second, treatment-specific optimism alone
predicted few changes in post-treatment health-related quality of life outcomes. Yet
the manner in which treatment-specific optimism related to post-treatment quality of
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life outcomes presents some apparent paradoxes. Although being optimistic about
one’s treatment predicted positive changes in post-treatment emotional well being,
greater treatment-specific optimism was related to a decline in post-treatment
prostate cancer quality of life. These findings are in direct contrast to other studies
that found optimism to be protective to emotional and health outcomes (Carver et al.,
2005; Leedham, Meyerowitz, Muirhead, & Frist, 1995; Sheier & Carver, 1992;
Steginga & Occhipinti, 2006). Although much support is demonstrated for
dispositional optimism and good quality of life outcomes, some theorists purport that
there may be instances when too much optimism can result in worse adjustment,
especially when expectations are not met (Tennen & Affleck, 1987). In terms of
physical health, few studies unexpectedly found that optimism was inversely
correlated with healthy birth (Chesterman, Cohen, & Adler, 1990) and
immunocompetence (Cohen et al., 1997). However, no resolution for these apparent
inconsistencies concerning positive expectations and divergent outcomes has been
identified (Sheier & Carver, 1992).
Another explanation for these findings may be found in the work of Cantor
and Norem (Canter & Norem, 1989; Canter, Norem, Niedenthal, Langston, &
Brower, 1987), regarding how pessimistic expectations can buffer against possible
outcome failures. These authors state that having low expectations regarding
outcomes can serve as a “self-protective goal” over the short term. This cognitive
processing is discussed as a characteristic of the defensive pessimist (Showers,
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1992). And in this current study, health-related quality of life was measured one
month after treatment, certainly representing a short period of time after treatment
when patients undoubtedly are still experiencing troublesome side effects (Kunkel et
al., 2000; O’Rourke, 2001). However, treatment-specific optimism was
conceptualized primarily as a unidimensional construct of optimism for the purposes
of this study, and not as a measure encompassing optimism and pessimism
representing opposite ends of a single bipolar dimension. Others have identified that
optimism and pessimism do covary but may not be true opposites (Marshall et al.,
1992). Clearly, these findings warrant further investigation into the relationship
between patients’ expectations of their treatment and post-treatment quality of life,
given the malleable nature of attitudes and their impact on behavior and adjustment
(Ajzen, 2001).
A third possible interpretation is worth mentioning. A third variable, such as
treatment modality, was considered as a likely culprit, given its significant
relationship with treatment-specific optimism. Post-hoc ANOVA revealed that men
who received ADT, were the least optimistic about their treatment than other
patients. However, none of the post-hoc exploratory moderational analyses found
treatment modality to moderate the relationship between treatment-specific optimism
and post-treatment health-related quality of life. Given the trend in negative
outcomes that treatment-specific optimism may pose, this topic may be worthy of
further study. The possible explanations discussed above could be tested using the
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treatment-specific optimism measure to observe how it may relate to some of these
other factors, and how they – in turn – may contribute to patients’ post-treatment
health-related quality of life.
Summary
This study provided a depiction of the relationships between patient
participation in treatment decision making and post-treatment health-related quality
of life. The data concerning these relationships underscore the complex nature that
treatment decision making and quality of life each entail, as well as the complexity
of how they relate to each other. In general, the relationships between treatment
decision making processes and post-treatment quality of life outcomes were mostly
positive, with exceptions of actual patient involvement in decision making and
treatment-specific optimism both negatively relating to prostate cancer quality of
life. With regard to patient participation in treatment decision making contributing to
changes in health-related quality of life after controlling for pretreatment factors, we
found that measures of the treatment decision making process were generally poor
predictors of changes to post-treatment quality of life outcomes. The data from this
study suggested that patients’ participation in the treatment decision making process
may not always have an impact on adjustment to quality of life, and in some cases its
impact may be negatively experienced.
Although most men reported being very active in the treatment decision
making process, in some cases patients appeared to experience their treatment
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decision making as a function of their age and medical characteristics as indicated by
exploratory findings. Given that this is the first study (1) to evaluate and to
operationalize various aspects of the treatment decision making process using
interval measures that have adequate factor structures and internal consistency and
(2) to test its hypotheses through a prospective, longitudinal design considering the
effects of other factors, raises questions for the widely held assumption that patient
involvement in treatment decision making is beneficial to post-treatment health-
related quality of life. Where others have reported conclusions supporting the
benefits of patient participation in treatment decision making, this study did not find
support for these claims and in some instances found contrary evidence in quality of
life adjustment. A striking difference between this study and others was its
controlling for pretreatment quality of life in order to observe changes to post-
treatment quality of life based on treatment decision making. This difference in
analytical approach may be a key reason for these differences in conclusions. An
annotated review of previous treatment decision making studies and their findings
have been compiled and listed in Table 22. Typical in much of the existing research
has been to utilize qualitative approaches to analyzing the data, to define treatment
decision making categorically, and to limit data collection in a cross-sectional
fashion. One of the strengths of this study was to approach investigating patient
treatment decision making in a rigorous and thorough fashion; this approach
170
certainly has yielded findings that do not conform to all the widely held assumptions
found in this new body of literature.
Limitations of the Study
Although the findings in this study expand on the suggestions, findings, and
assumptions of others, the current study’s data must be interpreted with caution.
Because the men in this study tended to report being highly involved in treatment
information communication, engaged in treatment choice deliberation, and very
optimistic about their treatment, the data were skewed; such restricted ranges, despite
efforts to normalize the data, provide limited variation necessary to interpret
correlational data meaningfully. Including a wide-range of patients’ experiences in
the treatment decision making process may yield more meaningful outcomes.
The current findings are limited by several important methodological considerations.
The demographic homogeneity of the current sample reduces the generalizability of
the findings. The convenience sample of POP participants lacked obvious ethnic and
socioeconomic diversity, comprising men who were nearly all Caucasian, highly
educated, and affluent. Findings from this homogeneous sample allow us to apply
the study results only to this small subset of the general population. Few studies have
specifically addressed the issues of socio-economic diversity among prostate cancer
patients, and found that African American men reported lower health-related quality
of life in comparison to Caucasians (Jenkins et al., 2004) and that prostate cancer
patients who were of Hispanic origin or had low-income were more likely to
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Table 22
List of Studies Conducted regarding Processes of Treatment Decision Making and Patient Quality of Life (QOL)
Group 1: Studies Observing the Relationship between Treatment Information Communication and Quality of Life.
Study Sample Study Purpose Study Design and Method Results
Joseph, Thibault,
& Ruttle-King
(2006)
136 Prostate
Cancer Patients
To observe how stress is related
to patient quality of life and
adjustment.
Retrospective at 1-5 years post-
diagnosis
Descriptive and Correlational
Analyses, no Covariates
included
Stress was not related to post-treatment
quality of life. Higher levels of stress were
related to less pretreatment treatment
information.
Wong, Stewart,
Dancey, Meana et
al., (2000)
101 Prostate
Cancer Patients
To assess the amount of
information patients want and
their desire to participate in
treatment decision making.
Cross-Sectional at pretreatment
Descriptive and Correlational
Analyses, no Covariates
included
The majority of patients desired treatment
information and desired active participation
in decision making. Greater information
was related to greater sense of control and
better mood.
Liang, Burnett,
Rowland, Meropol
et al., (2002)
613 Breast Cancer
Patients
To determine whether treatment
communication would relate to
treatment satisfaction.
Retrospective at 2 months post-
treatment
Logistic Regressions,
Covariates included patient &
physician factors
Neither patient-initiated communication nor
physician attitude was related to treatment
satisfaction. Physician-initiated
communication was significantly related to
patient satisfaction. Older patients were 2.4
times more likely to be satisfied than their
younger counterparts.
172
Table 22 (continued)
List of Studies Conducted regarding Processes of Treatment Decision Making and Patient Quality of Life (QOL)
Group 2: Studies Observing the Relationship between Treatment Decision Making Processes and Quality of Life.
Morris & Royle
(1988)
30 Breast Cancer
Patients
To determine whether having a
choice of treatment is related to
pre- and post-treatment anxiety
and depression.
Prospective at pretreatment &
2-6 months post-treatment
Analyses of Variance, no
Covariates included
Patients not offered a choice of treatment
reported higher levels of anxiety and
depression at pre- and post-treatment than
did those who had a choice.
Fallowfield, Hall,
Maguire, Baum &
A’Hem (1994)
216 Breast Cancer
Patients
To determine whether patients
offered a choice of treatment
would have better psychological
adjustment.
Prospective at pretreatment & 3
years post-treatment
Descriptive and Correlational
Analyses, no Covariates
included
No psychological adjustment differences
were found between patients offered a
choice of treatment or not.
Hack, Degner,
Watson, & Sinha
(2006)
278 Breast Cancer
Patients
To evaluate whether active
pretreatment decision making
would relate to better quality of
life at post-treatment.
Prospective at time of treatment
& 3 years post-treatment
Descriptive and Chi-Square
Analyses, no Covariates
included
Those who were active participants in
treatment decision making reported better
overall quality of life at 3 years post-
treatment.
Davison, Gleave,
Goldenberg,
Degner et al.,
(2002)
80 Prostate Cancer
Patients
To compare treatment decisional
and informational preferences
among patients.
Cross-Sectional at time of
diagnosis
Descriptive and Qualitative
(Unfolding Theory)
Half of patients preferred to be active
participants in their treatment decision
making, whereas fewer than half preferred
to be collaborators. No relationship was
found between decision making and
information preferences.
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Table 22 (continued)
List of Studies Conducted regarding Processes of Treatment Decision Making and Patient Quality of Life (QOL)
Group 3: Studies Observing the Relationship between Treatment Decision Making, Coping and Quality of Life.
Study Sample Study Purpose Study Design and Method Results
Petersen, Heesaker
& de Witt Marsh
(2001)
120 Mixed Cancer
Patients, 64%
Female
To observe how treatment
decisional styles are related to
disease coping.
Cross-Sectional at time of
treatment
Analyses of Variance, no
Covariates included
Deeper processing in decision making was
related to greater use of effective coping
strategies.
Truant & Bottorff
(1999)
16 Breast Cancer
Patients
To examine the treatment
decision making processes of
those choosing complementary
treatments.
Retrospective after treatment
Qualitative (Grounded Theory),
no Covariates included
The act of actively choosing one’s treatment
was interpreted as a form of coping as
regaining control over cancer.
Group 4: Studies Observing the Relationship between Treatment Decision Making Consonance and Quality of Life.
Gattellari, Botow
& Tattersall (2001)
233 Mixed Cancer
Patients
To determine whether tailored
treatment decision making was
protective of anxiety and
treatment satisfaction.
Cross-Sectional at pretreatment
Analyses of Variance,
Covariates included
pretreatment anxiety.
Linear Regressions, Covariates
included decision making
preferences.
Dissonance in treatment decision making
was related to greater anxiety and less
satisfaction. Those who were consonant in
treatment decision making reported lower
anxiety and more satisfaction with medical
consult than those who were not.
Lantz, Janz,
Schwartz, et al.,
(2005)
1633 Breast
Cancer Patients
To evaluate the patterns of
satisfaction with treatment choice
and decision making.
Retrospective, post treatment
Logistic Regressions,
Covariates included
demographic & medical
variables.
Consonance in treatment decision making
predicted satisfaction with treatment choice.
Dissonance in decision making predicted
low satisfaction and treatment regret.
174
Table 22 (continued)
List of Studies Conducted regarding Processes of Treatment Decision Making and Patient Quality of Life (QOL)
Group 5: Studies Observing the Relationship between Treatment Specific Optimism and Quality of Life.
Stanton, Estes,
Estes, Cameron et
al., (1998)
56 Breast Cancer
Patients
To determine that treatment
decision making and treatment
expectations would be related to
post-treatment psychosocial
adjustment.
Prospective at the time of
treatment & 3 months to 1 year
post-treatment
Repeated Measures Analyses
of Variance & Multiple
Regressions, covariates
included pretreatment
psychosocial adjustment.
Discrepancy between expectations and
perceived outcomes predicted post-
treatment maladjustment at 3 months and
one year.
Cohen, de Moor,
& Amato (2001)
46 Renal and
Melanoma Cancer
Patients
To assess that treatment specific
optimism would be related to
depression and psychological
distress.
Prospective at pretreatment & 3
weeks post-treatment
Multiple Regressions,
Covariates included age,
medical factors, & pretreatment
mood.
Patient with high treatment specific
optimism were found to report better mental
health at pretreatment and at post-treatment.
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experience worse health-related quality of on all domains than the general age-
matched population (Krupski et al., 2004). How prostate cancer patients from
different ethnic backgrounds or from various socio-economic brackets participate in
their treatment decision making and are affected by this process, has not been
studied; yet such investigation deserves special consideration given that involvement
in one’s treatment decision making process may be shaped by cultural or socio-
economic beliefs. Another example of skewed data evident in the current study was
the tendency of patients to report favorably to most measures. A snapshot of the
prostate cancer patients in this study would depict a group of patients who were
generally happy, healthy, and active participants in their treatment decision making.
A notable amount of the data was negatively skewed, and despite efforts to transform
and center variables, restricted ranges could have underestimated the findings
(Pedhazur, 1997).
A review of the attrition and retention analyses also confirms the likelihood
that the data were skewed. Because radiation patients were those less likely to
participate in the study, the question remains concerning how the participating
radiation patients might have differed in their treatment decision making and quality
of life experiences from those radiation patients who chose not to participate.
Although there were few differences between the participating and non-participating
radiation patients on decision making and treatment-specific optimism variables,
these findings do not provide ample evidence that there were in fact meaningful
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differences which could have ultimately contributed to the skewed results.
Additional contributions to the biased sample can be observed in the differences
between the participants and drop-outs. Those who did not return their one-month
post-treatment questionnaire on time were less educated, had less income, were
older, and had higher Gleason scores than participants. These findings underscore the
point discussed above concerning the general bias in participants’ education and
income. Furthermore, participants were younger and had less severe disease, which
may help explain why the sample was skewed toward reports of better quality of life
and high degrees of involvement in the treatment decision making process.
The size of the sample is a serious limitation. Multiple regression analyses in
this study were designed to control for numerous confounds in order to observe the
unique contributions of the various aspects of patient treatment decision making. The
small number of participants severely limited power to use such elaborate
approaches, as well as the numerous analyses performed addressing each objective.
Statistical power analyses indicated that we had power (79%) to detect effects sizes
in the medium range (r = 0.35 or higher) in correlational methods, and power (78%)
to detect a critical F of 2.78 or more for small effect sizes (r = 0.20 or less) in
multiple regression analyses using 3 predictor variables. Unfortunately, most of the
regression analyses required more than 3 predictor variables; and power for those
regressions with 5 predictors was 0.70 for an F of 2.40, and 0.65 for 6 predictors.
However, the significant findings that did emerge in the data may suggest that
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particularly strong relationships may exist for these patients with prostate cancer.
Clearly, future research involving more representative and appropriately sized
samples will be necessary to assess the generalizability of these findings.
Finally, despite the efforts to conceptualize the treatment decision making
process within an accepted theoretical framework, several methodological oversights
may cast doubt upon the accuracy of the findings. First, measures were self-report.
Although this is a common method to obtain data in behavioral research, this
approach is not without its limitation and threats to external validity; namely, self-
report measures can yield responses that are of a social desirable nature (Rosenthal &
Rosnow, 1975). Another shortcoming is that the initial questionnaire did not account
for whether patients were offered more than one type of treatment. An important
assumption in decision making is based on decisional conflict (having to choose
between two alternatives). Although all prostate cancer patients will be faced with a
decisional conflict of choosing to treat or not to treat, having determined those who
actually had to deliberate over two (or more) treatment modalities would have
provided additional useful information to understanding patient treatment decision
making. Another oversight was that we assumed that the patients had already made
their treatment choice by the time they completed the pretreatment questionnaire.
There is a possibility that some patients completed these surveys prior to having
made their treatment decision. Not knowing whether item responses reflected the
same point in time among patients (i.e., having already selected one’s treatment
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choice), reduces the assumed construct validity of the relevant measures as well as
the internal validity of the study. And last, although the data concerning physician
referral was collected, we did not capture the oncologist or clinic from which
patients based their treatment decision making process. Although it is likely that
most treatments were conducted by a small number of oncologists in this sample, we
had not accounted for the possibility of a doctor-by-treatment-modality confound
affecting the data. Treatment modality was controlled for in many main analyses, but
a doctor-by-treatment-modality effect could have altered the data in ways for which
we could not have controlled.
Conclusions
Despite the drawbacks, this study provides one of the most comprehensive
assessments of prostate cancer patients’ experiences in the treatment decision
making process and related health-related quality of life. The study’s strengths were
to utilize its prospective, longitudinal design and to design multivariate analyses to
assess the unique contributions that the treatment decision making process may have
on men’s post-treatment health-related quality of life. The study is the first to
evaluate the treatment decision making process using continuous measures designed
to consider patient participation in the treatment decision making process as a
multifaceted event.
The findings suggested that the prostate cancer patients in this study were
active participants in their treatment decision making process, were satisfied with
179
their treatment choices and decision making involvement, and were well adjusted to
the disease and its treatment. Our data also indicated that the treatment decision
making process is a multifaceted process as anticipated, and that not all aspects of
the process related to overall patient post-treatment quality or changes to it. In some
instances patients were observed to experience their treatment decision making
process as a function of their treatment modality, which speaks to the importance of
tailoring treatment decision making to patient needs or preferences. By and large, the
most interesting finding from this study was that among those decision making
behaviors that did predict changes in post-treatment quality of life, it is possible that
some aspects of choice deliberation and treatment-specific optimism may negatively
affect patients’ prostate cancer quality of life adjustment. Thus medical professionals
should not assume the overarching benefits that previous studies have claimed
regarding patient involvement in treatment decision making, especially from those
studies which conceived the process in gross, oversimplified terms. Future research
should move beyond the exploratory and descriptive data provided here by further
testing the study-developed measures through rigorous validity research and by
expanding the research program to encompass larger, more representative samples of
patients. Such research efforts could provide patients – and medical professionals
alike – with a definitive understanding of what it means to patients, suffering from
any disease, to have to contend with participating in the process of making their own
treatment decisions.
180
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195
APPENDIX A
List of Measures Used in the Current Study
One Month
Pre-
Treatment
Post-
Treatment
(Time 1) (Time 2)
Demographic and Medical Variables
PREDICTOR MEASURES
Treatment Information Communication
Communication with Medical Team (CMT)
Patient Perceived Understanding (PPU)
Patient Appraisal of Physician Communication Effectiveness
(PACE)
Treatment Choice Deliberation
Factors Influencing Treatment Choice (FITC)
Involvement in Treatment Decision Making (ITDM)
Problem-Focused Coping (PF-COPE)
Treatment Choice Appraisal
Treatment Decision Making Consonance (TDMC)
Satisfaction with Treatment Choice (SWC)
Satisfaction with Decision Making Involvement (SWI)
Treatment-Specific Optimism (TSO)
Dispositional Optimism (LOT)
CRITERION MEASURES
General Quality of Life
Functional Assessment of Cancer Therapy, (FACT)
General Well Being
Emotional Well Being
Functional Well Being
Physical Well Being
Social Well Being
196
APPENDIX A (continued)
List of Measures Used in the Current Study
One Month
Pre-
Treatment
Post-
Treatment
(Time 1) (Time 2)
Prostate Cancer Quality of Life
Functional Assessment of Cancer Therapy, Prostate (FACT-P)
Prostate Cancer Well Being
Prostate Cancer Index (PCI)
Bowel Functioning
Sexual Functioning
Urinary Functioning
Psychosocial Adjustment
Stress: Impact of Events Scale (IES-R)
Mood: Positive and Negative Affect Scale (PANAS)
Depression: Centers for Epidemiological Research (CES-D)
197
APPENDIX B
Refusal Questionnaire

CALLER says: “With respect to the quality of your life during the time since your prostate cancer treatment…”

Not at all
A little bit
Some what
Quite a bit
1.
How much would you say having PROSTATE CANCER has
affected your quality of life?

Would you say…..
Very much
No control whatsoever
A little bit of control
Some control over tx
Quite a bit of control
2.
To what extent did (do) you have control in deciding your
prostate cancer treatment?

Would you say…..
Total control over tx
Not at all confident
A little bit
Moderately confident
Quite a bit
3.
How confident are you that your prostate cancer
treatment is the best choice for you?

Would you say…..
Extremely confident
Not at all likely
Slightly
Moderately
Very likely
4.
How likely is it that the treatment you had (will
have) for your prostate cancer will cure your
cancer altogether?

Would you say….
Extremely likely
Not at all likely
Slightly
Moderately
Very likely
5.
How likely is it that the treatment you had (will
have) for your prostate cancer will result in
additional treatments over time?

Would you say…..
Extremely likely
198
APPENDIX B (continued)
Refusal Questionnaire

CALLER says: “With respect to the quality of your life during the time since your prostate cancer treatment…”

Not at all likely
Slightly
Moderately
Very likely
6.
How likely is it that the treatment you had (will
have) for your prostate cancer will result in
recurrence if cancer?

Would you say…..
Extremely likely
Not at all stressful
Slightly stressful
Moderately stressful
Very stressful
7.
Overall, how stressful did (are) you find(ing) the
treatment decision making process?

Would you say…..
Extremely stressful
199
APPENDIX C.1
Legend:
CMT = Communication w/ Medical Team (10 items)
PPU = Patient Perceived Understanding (4 items)
ACE = Appraisal of Self Communication Effectiveness (4 items)
PACE = Patient Appraisal of Physician Communication Effectiveness (5 items)
Medical Team Communication
The following questions are regarding your interaction with your medical team (if you have mainly interacted with only one person (such as
your doctor), please consider that person when answering these questions). For each question, please circle the number that best represents
your current experience with prostate cancer and its treatment. You may circle any one of the numbers (from 1 to 7) on each scale that best
represents the strength of your feelings or opinions.
1. How much information did you receive from the medical team regarding your prostate cancer treatment?(reversed scored)
1 2 3 4 5 6 7
I received too I received the right I did not receive
much information amount of information enough information
2. How well do you understand the information you have acquired or received?
1 2 3 4 5 6 7
No understanding Some Complete
at all understanding understanding
3. How satisfactory is this information in meeting your needs concerning your treatment for prostate cancer?(reversed scored)
1 2 3 4 5 6 7
Extremely Somewhat Not at all
satisfactory satisfactory satisfactory
CMT 1
PPU 1
PPU 2
200
APPENDIX C.1 (continued)
4a-e. How much did you communicate with the medical team about:
4a. Cancer
1 2 3 4 5 6 7
Very Very
little much
4b. Sexual Functioning
1 2 3 4 5 6 7
Very Very
little much
4c. Urinary Incontinence
1 2 3 4 5 6 7
Very Very
little much
4d. Bowel Problems
1 2 3 4 5 6 7
Very Very
little much
4e. My Treatment
1 2 3 4 5 6 7
Very Very
little much
CMT 2
CMT 3
CMT 4
CMT 5
CMT 6
201
APPENDIX C.1 (continued)
5. When communicating with the medical team about prostate cancer, how often did you ask questions (as opposed to just
receiving information that the team shared)? (reversed scored)
1 2 3 4 5 6 7
I always asked I sometimes asked I never asked
questions questions questions
6. How confident are you that in the future you will be able to obtain desired information from the medical team?
1 2 3 4 5 6 7
Not at all Moderately Completely
confident confident confident
Questions 7a-e answered on this scale:
7a-e. How would you describe your medical team's ability to:
7a. Discuss with you your choices for prostate cancer treatment.
7b. Explain to you the differences in your treatment options.
7c. Treat you respectfully
7d. Listen to your concerns
7e. Not rush through the medical appointments.
1 2 3 4 5 6 7
Poor Average Excellent
ability ability ability
CMT 7
CMT 8
PACE 1 – PACE 5
202
APPENDIX C.1 (continued)
8. Were there issues that you would have wanted to discuss with the medical team but did not? (reversed scored)
1 2 3 4 5 6 7
I discussed all I discussed some I did not discuss
the issues I wanted of the issues I wanted any issue(s) I wanted
9. How well do you understand the possible advantages and disadvantages that prostate cancer treatments may cause?
1 2 3 4 5 6 7
Don't understand Somewhat Completely
at all understand understand
10. Overall, how much control did you feel you had over the information process (gathering and receiving information from
relevant sources)?
1 2 3 4 5 6 7
Very Very
little much
11. Overall, how prepared do you feel about what to expect with regard to possible changes to your health and lifestyle after
treatment?
1 2 3 4 5 6 7
Not at all Somewhat Completely
prepared prepared prepared
CMT 9
PPU3
CMT 10
PPU4
203
APPENDIX C.1 (continued)
12a-d. How would you describe your ability to:
12a. Obtain information from your medical team or physician.
1 2 3 4 5 6 7
Poor Average Excellent
ability ability ability
12b. Communicate to your doctor your personal preferences regarding treatment options.
1 2 3 4 5 6 7
Poor Average Excellent
ability ability ability
12c. Get your physician to adjust treatment plans to meet your needs.
1 2 3 4 5 6 7
Poor Average Excellent
ability ability ability
12d. Understand the information about your diagnosis and the effects of treatment.
1 2 3 4 5 6 7
Poor Average Excellent
ability ability ability
ACE 1
ACE 2
ACE 3
ACE 4
204
APPENDIX C.2
Factor Analysis of the Scales and Items of Treatment Information Communication
Component Reliability
Item 1 2 3 Coefficient

Patient Perceived Understanding (PPU) 0.755
How well do you understand the information you
have acquired or received? 0.100 0.250 0.786
How satisfactory is this information in meeting
your need concerning your treatment for prostate
cancer? 0.602 0.102 0.219
How well do you understand the possible
advantages and disadvantages that prostate
cancer treatments may cause? 0.196 0.084 0.882
Overall, how prepared do you feel about what to
expect with regard to possible changes to your
health and lifestyle 0.357 0.183 0.634

Patient Communication Effectiveness (CSE)* . . 0.859
How would you describe your ability to:
Obtain information from your medical team or
physician. 0.368 0.814 0.170
Communicate to your doctor your personal
preferences regarding treatment options. 0.234 0.795 0.400
Get your physician to adjust treatment plans to
meet your needs. 0.363 0.777 0.009
Understand the information about your diagnosis
and the effects of treatment. 0.251 0.672 0.476

Patient Appraisal of Physician
Communication (PCE)* . . 0.935
How would you describe your medical teams'
ability to:
Discuss with you your choices for prostate cancer
treatment. 0.655 0.442 0.265
Explain to you the differences in your treatment
options. 0.824 0.405 0.142
Treat you respectfully. 0.880 0.286 0.121
Listen to your concerns. 0.869 0.282 0.152
Not rush through the medical appointments. 0.755 0.266 0.290

Eigenvalue 7.142 1.469 1.048
Variance (%) 31.77 23.50 19.02

Note:
* Item responses, 1 - 7, were anchored with:
1="poor ability" and 7="excellent ability"
205
APPENDIX D
Factors Influencing Treatment Choice

Various factors can influence one’s medical decisions. For each item below, please
indicate the extent to which each matter played a role in your deciding what
treatment was best for you. Please feel free to describe further your reasons in the
space below.
(circle one number on each line)
Not
influential
Somewhat
influential

Moderately
influential

Very
influential
1. Career concerns or obligations………………..
1 2 3 4
2. Doctor recommendations….…………..……....
1 2 3 4
3. Factors related to post-treatment disability…....
1 2 3 4
4. Family opinions………………………...……..
1 2 3 4
5. Financial worries or concerns…………..……..
1 2 3 4
6. Length of recovery….….………….…….…….
1 2 3 4
7. Length of treatment………..…………..……....
1 2 3 4
8. Meeting family responsibilities..…………..…..
1 2 3 4
9. Physicians' opinions……….....…………....…..
1 2 3 4
10. Religious beliefs or practices…………….…....
1 2 3 4
11. Travel plans…………………………..…..…....
1 2 3 4
12. Treatment scheduling issues……………..…....
1 2 3 4
13. Treatment side effects….………….…….…….
1 2 3 4
Additional matters or further comments:
206
APPENDIX E.1
Decision Making

The following questions are about your prostate cancer treatment and the thoughts and
considerations you may have in deciding the medical procedure you will have. Please circle the
response that best answers the questions below concerning your prostate cancer treatment as they
apply to you today.
(circle one number on each line)
Not at
all

Slightly /
To a little
extent

Moderately
/
To some
extent

Very /
To much
extent

Extremely /
To a great
extent
1. To what extent did you have control in deciding
your prostate cancer treatment?

1 2 3 4 5
2. How confident are you that the prostate cancer
treatment you chose is the best for you?

1 2 3 4 5
3. Did you exercise choice concerning your
medical treatment?

1 2 3 4 5
4. Overall, how involved were you in making
decisions about your prostate cancer treatment?

1
2 3 4 5
5. Overall, how satisfied are you with the role you
played during the treatment decision making
process?

1 2 3 4 5
6. How active were you in deciding on your
prostate cancer treatment?

1 2 3 4 5
7. Overall, how satisfied are you with the final
treatment choice to treat your prostate cancer?

1
2 3 4 5
8. How much did you participate in your treatment
decision making process?
1 2 3 4 5
ITDM-1
ITDM-2
ITDM-3
ITDM-4
SWC-1
SWC-2
SWI-1
ITDM-5
207
APPENDIX E.1 (continued)
Not at
all

Slightly /
To a little
extent

Moderately
/
To some
extent

Very /
To much
extent

Extremely /
To a great
extent
9. Did you participate in the treatment decision
making process to the extent that you wished?
1 2 3 4 5
10. To what extent did you deliberate over your
prostate cancer treatment options or choices?

1 2 3 4 5
11. Were you as involved in your treatment decision
making to the extent that you wished?

1 2 3 4 5
12. To what extent did you discuss with others your
prostate treatment choices?

1 2 3 4 5
13. How much do you worry that you made the
wrong treatment choice for your prostate cancer?
(reversed scored)
1 2 3 4 5
14. To what extent did you involve others in helping
to decide which treatment was best for you?

1 2 3 4 5
ITDM-6
ITDM-7
SWC-3
ITDM-8
SWI-3
SWI-2
208
APPENDIX E.2
Factor Analysis of the Items of Patient Involvement in Treatment Decision
Making
Component Reliability
Item* 1 2 Coefficient**
Autonomy 0.923
To what extent did you have control in deciding your prostate cancer treatment? 0.819 0.165
Did you exercise choice concerning your medical treatment? 0.894 0.249
Overall, how involved were you in making decisions about your prostate cancer
treatment? 0.904 0.189
How active were you in deciding on your prostate cancer treatment? 0.871 0.256
How much did you participate in your treatment decision making process? 0.856 0.311
To what extent did you deliberate over your prostate cancer treatment options or choices? 0.626 0.228
Collaboration . 0.691
To what extent did you discuss with others your prostate treatment choices? 0.337 0.783
To what extent did you involve others in helping to decide which treatment was best for
you? 0.150 0.892
Eigenvalue 5.043 1.011
Variance (%) 53.83 21.84
Note:
* Item responses were: 1="not at all", 2="slightly", 3="moderately", 4="very", 5="extremely"
** Total scale summary alpha was .896
209
APPENDIX E.3
Factor Analysis of the Items of Satisfaction with Treatment Choice and Satisfaction with Treatment Decision Making
Involvement
Component Reliability
Item* 1 2 Coefficient
Satisfaction with Treatment Choice 0.800
How confident are you that the prostate cancer treatment you chose is the best for you? 0.904 0.138
Overall, how satisfied are you with the final treatment choice to treat your prostate cancer? 0.755 0.448
How much do you worry that you made the wrong treatment choice for your prostate cancer?
(Reverse scored) 0.834 -0.092
Satisfaction with Treatment Decision Making Involvement . 0.781
Overall, how satisfied are you with the role you played during the treatment decision making
process? 0.420 0.649
Did you participate in the treatment decision making process to the extent that you wished? 0.010 0.823
Were you as involved in your treatment decision making to the extent that you wished? 0.044 0.936
Eigenvalue 2.942 1.522
Variance (%) 37.70 36.70
Note:
* Item responses were: 1="not at all", 2="slightly", 3="moderately", 4="very", 5="extremely"
210
APPENDIX F
Treatment Decision Making Consonance
Measure 2 – Measure 1 = Composite Score

15. Please read the following five (5) statements about how your prostate cancer treatment
decision making process came about. Please check only one statement that best
describes how this process actually took place for you with respect to your treatment:

____ A. I made all the decisions about which treatment I will receive.
____ B. I made the final decision about my treatment after seriously
considering my doctor's opinions.

____ C. My doctor and I shared the responsibility for deciding which
treatment is best for me.

____ D. My doctor made the final decision about my treatment after
seriously considering my opinions.

____ E. My doctor made all the decisions about which treatment I will
receive.

16. The next following five (5) statements differ from above in that they address how the
treatment decision making process ideally should take place. Please check only one
statement that best describes how you would have preferred your treatment decision
making to have come about:

____ A. I should have made all the decisions about which treatment I will
receive.
____ B. I should have made the final decision about my treatment after
seriously considering my doctor's opinions.

____ C. My doctor and I should have shared the responsibility for deciding
which treatment is best for me.

____ D. My doctor should have made the final decision about my treatment
after seriously considering my opinions.

____ E. My doctor should have made all the decisions about which treatment
I will receive.

211
APPENDIX G.1
Treatment Specific Optimism Scale

Please circle the response that best describes your reactions to the italicized
question below concerning your prostate cancer treatment.

How likely is it that the treatment you will have for your prostate cancer:

(circle one number on each line)
Not at all

Slightly

Moderately Very

Extremely
1. Will cure your cancer altogether?

1 2 3 4 5
2. Will result in additional
treatments over time? (reversed scored)
1 2 3 4 5
3. Will result in recurrence of
cancer? (reversed scored)
1 2 3 4 5
4. Will result in possible unpleasant side
effects? (Filler, reversed scored)
1 2 3 4 5
5. Will result in treatment-related
disability (or loss of function)?
(Filler, reversed scored)
1 2 3 4 5
6. Will increase your life span?

1 2 3 4 5
7. Will work better than any other
treatment available?

1 2 3 4 5
212
APPENDIX G.2
Factor Analysis of the Items of the Treatment Specific Optimism Scale

Component Reliability
Item* 1 2 Coefficient
5-item Treatment Specific Optimism (TSO) 0.659
How likely is it that the treatment you will have for
your prostate cancer:
Will cure your cancer altogether? 0.776 0.184
Will result in additional treatment over time? (Reverse
scored) 0.860 0.026
Will result in the recurrence of cancer? (Reverse
scored) 0.735 0.041
Will increase your life span? 0.268 0.822
Will work better than any other treatment available? -0.062 0.878

Eigenvalue 2.152 1.289
Variance (%) 39.14 29.68

Component Reliability
Item* 1 Coefficient
4-item Treatment Specific Optimism (TSO) 0.689
How likely is it that the treatment you will have for
your prostate cancer:
Will cure your cancer altogether? 0.805
Will result in additional treatment over time? (Reverse
scored) 0.82
Will result in the recurrence of cancer? (Reverse
scored) 0.691
Will increase your life span? 0.536

Eigenvalue 2.086
Variance (%) 52.16

Note:
* Item responses were: 1="not at all", 2="slightly", 3="moderately", 4="very", 5="extremely"

Abstract (if available)

Abstract Research indicates that post-treatment health-related quality of life (HRQL) of cancer patients may be enhanced when patients participate in their own treatment decision making (TDM) process. This conception is based on a short history of research that has operationalized and has measured patient TDM often without the guidance of an accepted theoretical framework. The current study set out to operationalize and to design measures of the patient TDM process based on informed decision making theory comprising three measurable aspects of treatment information communication, treatment choice deliberation, and treatment choice appraisal. Exploratory factor analyses demonstrated that these measures possess good factor structures and internal reliability. In this study, measures of the TDM process and HRQL of 57 prostate cancer patients were examined prior to primary treatment and HRQL was examined again at one-month post-treatment, in order to determine whether patient participation in the TDM process would predict improvement to HRQL. Residualized hierarchical regression analyses revealed that the three aspects of the patient TDM process predicted few changes in HRQL outcomes. Despite the lack of support for the benefits of patient participation in TDM, the prostate cancer patients in this study reported having ample treatment information, being very involved in their treatment decision making, being very satisfied with their treatment choices, and having positive expectations about treatment outcomes. The men also reported having good HRQL at both time points. Post-hoc exploratory correlational and moderational analyses indicated that in some instances TDM participation may be a function of patients' treatment modality, age, or their disease severity. Implications for further research are discussed.

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Asset Metadata

Creator Giedzinska-Simons, Antoinette Stephanie (author)

Core Title Health-related quality of life correlates of the treatment decision making process of newly diagnosed prostate cancer patients

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 08/01/2007

Defense Date 11/06/2006

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag OAI-PMH Harvest,prostate cancer,Quality of life,treatment decision making

Language English

Advisor Meyerowitz, Beth E. (committee chair), Gatz, Margaret (committee member), Knight, Bob (committee member), Richardson, Jean L. (committee member), Walsh, David A. (committee member)

Creator Email giedzins@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m740

Unique identifier UC1295245

Identifier etd-GiedzinskaSimons-20070801 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-546118 (legacy record id),usctheses-m740 (legacy record id)

Legacy Identifier etd-GiedzinskaSimons-20070801.pdf

Dmrecord 546118

Document Type Dissertation

Rights Giedzinska-Simons, Antoinette Stephanie

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu