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Analysis of unmet needs for young cancer survivors using multinomial logistic regression
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Analysis of unmet needs for young cancer survivors using multinomial logistic regression
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Analysis of Unmet Needs for Young Cancer Survivors using Multinomial Logistic Regression by ShiYu Shen A Thesis Presented to the FACULTY OF THE USC KECK SCHOOL OF MEDICINE UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (BIOSTATISTICS) May 2021 COPYRIGHT 2021 Shiyu Shen TABLEOFCONTENTS List of Tables iv Abstract v Chapter 1: Introduction 1 1.1 Background 1 1.2 Problem statement 2 1.3 Literature Review 4 Chapter 2: Methodology 6 2.1 Chi-square test 6 2.2 Multinomial Logistic Regression 7 2.3 Likelihood ratio test 9 2.4 Akaike information criterion 9 2.5 Bayesian information criterion 10 Chapter 3: Results 12 3.1 Descriptive statistics 12 3.2 Chi-square test and Univariate Analysis 15 3.3 Multivariable MLR model building 17 3.4 Interpretations of MLR models 17 ii Chapter 4: Discussion and Conclusion 23 4.1 Discussion 23 4.2 Conclusion 24 Bibliography 25 Appendix 28 Appendix A: STATA Code 28 iii ListofTables 3.1 Basic Characteristics of Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Cancer information of Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Chi-square test of potential predictors and response variables . . . . . . . . . . . . 16 3.4 Potential predictors for each response variable . . . . . . . . . . . . . . . . . . . . 17 3.5 AIC and BIC for potential models . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.6 Models of Unmet Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.7 Models of Used Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 iv AnalysisofUnmetNeedsforYoungCancerSurvivorsusing MultinomialLogisticRegression ShiYu Shen Abstract In the United States, thousands of adolescents and young adults are being diagnosed with cancer. There has been a great medical concern for a very long time and researchers have put so much effort into improving it. However, feedback from young cancer survivors shows that needs and services for cancer treatment are still unmet. Therefore, to know what services have been fully used and what needs have not been met yet is of great importance. In this study, multinomial logic regression is applied to models for unmet needs and services, which was built on demographic characteristics and cancer treatment from 215 young cancer survivors. It is found that all kinds of services have been reported as unmet needs by different groups of people. However, for different groups of people, their needs for services are different. Therefore, specific services should be more applied to typical groups of patients in order to meet their needs and thereby help them live a better life. Keywords: Adolescents and young adult, cancer survivors, Unmet needs, Used services, Multinomial Logistic Regression v Chapter1 Introduction 1.1 Background According to statistics from the World Health Organization (WHO), cancer has become the second leading cause for death around the world (2018). Among all the cancer diagnoses, 5% comes from adolescents and young adults, which is about 8 times the number of cases in children (2020). Within the United States, around 5,000 to 6,000 adolescents are being diagnosed with cancer annually (2021), which becomes a terrible problem for public health. However, there are already some improvements in the field after researchers’ great efforts in enhancing overall survival rate from cancer for adolescents. With the development of medical technologies, various treatments have been applied for cancer patients, including surgery, radiation therapy, chemotherapy and other drugs (2019). Even though cancer treatments have released a great part of the pain for patients, there are still some services that are not available for all and need to be improved. The true problems in the field include—to know what services are practical and useful for cancer patients, to understand what 1 services that have not been met for cancer patients, to maximize the need that can help psychologic -ally for cancer patients. The problems need to be solved to improve the quality of life for cancer patients. Therefore, it is significant to analyze unmet needs and services for cancer patients with different characteristics. 1.2 Problemstatement In fact, unmet needs among young cancer survivors (ages from 15 to 39) are prevalent, based on information collected by questionnaires (Carey, McHarg, Sanson-Fisher, & Shackshaft, 2021). However, further psychometric testing of the measure for specific needs is needed. In order to help provide a better life for young cancer survivors, it is important to understand what are the unmet needs and services used by young cancer survivors with different characteristic. In this study, we will be analyzing differences of unmet needs and used services among young cancer survivors based on their demographic characteristics. In this study, we will be analyzing the data which comes from the article “ Psychosocial Service Use and Unmet Need Among Recently Diagnosed Adole -scent and Young Adult Cancer Patients ” (Zebrack et al., 2013). In Zebrack’s study, questionnaires were collected from 215 young cancer survivors in 3 pediatric care institutions (Doernbecher Children’s Hospital in Portland, Ore; Christus Santa Rosa Children’s Hospital, San Antonio, Tex; and Children’s Hospital, Los Angeles, Calif) and 2 university-affiliated adult care medical institutions (Oregon Health and Sciences University Hospital, Portland; and the University of Texas Health Science Center, San Antonio). Models of used service and unmet needs are built on the same groups of variables, which including gender, race (White/Non-white), employment status, relationship status, number 2 of symptoms, cancer type by survival rate (Low/High), chemotherapy, radiation and age. However, for each models of unmet needs and services, predictors which can better explain the models might be different. Therefore, in this study, variables are selected differently to build each models. Models in this study are based on two main kinds of information. The first one is basic characteristics of patients: age, gender, race (White/Hispanic/others), education status, employment status, househ -old income, relationship. Second is cancer information of patients: cancer type by survival rate (Low/Medium/High), chemotherapy, radiation and surgery. The response of the models are answers collected from the following questions: • services that provide information about illness, treatment, risks for recurr -ence, or second cancers; • services that offer cancer education or support appropriate for young cancer survivors online; • services that provide counseling about diet and nutrition; • services that offer cancer education or support appropriate for young cancer survivors in community centers, camps, retreats, or adventure programs; • services that provide counseling by mental health professionals; • services that offer guidance related to sexuality or intimacy; • services that help with understanding health insurance, disability, or social security; • services that offer infertility treatment; • services that give transportation assistance; 3 The choices for the questions above can be: Have used and would like to use more; Have used and have no further need; Have not used but would like to use; Have not used and have no need. In the study of Zebrack et al., they dichotomized the choices in the questionnaire and used binary logistic regression to analyze unmet needs and used services in adolescent and young adult cancer survivors. But in this study, the original choices are kept and only two choices are combined. Specifically, answers in Have used and would like to use more and Have used and have no further need are categorized as “service used”; answers in Have not used but would like to use are categorized as “service unmet”; answers in Have not used and have no need are categorized as “others”. Thus, multinomial logistic regression will be used instead of binary logistic regression in this study. 1.3 LiteratureReview Adolescent and young cancer survivors (ages 15-39) reported that needs related to medial information are highly unmet (McCarthy, McNeil, Drew, Orme & Sawyer, 2018). It is of great importance to promote the engagement of young cancer survivors in meeting needs in this field. When patients are provided with sufficient medical information about cancer, they can understand correctly the way of nursing themselves and thereby increase their exercise abilities (Jiang & Xia, 2009). This can also improve their life qualities and thereby increasing their life expansions. Young cancer survivors are faced with many issues about fertility and mental health (Patterson, McDonald, Zebrack & Medlow, 2015), so it is necessary to enhance the nursing style that can address psychosocial needs for adolescents and young cancer survivors. In Whitney’s study (2015), they found that the quality of mental health care has improved from 2005 to 2010, which shows 4 that the problem of unmet need for mental health might have been addressed and some progress might have been made. According to Zebrack’s study (2009), the supportive care services is unmet among young cancer survivors. Specifically, more than 50% of patients in their study reported unmet services related to infertility information, nutrition and retreat programs. Despite some unmet needs for young cancer survivors have been addressed, it is still a challenge to quantify the assessment (Lambert et al., 2012). For example, some higher unmet needs might caused by insufficient social support. Wang, Molassiotis and Chung also states in their study that examining unmet needs for patients still needs improvement (2018). Research emphases have shifted toward other topics and fewer attention has been paid to unmet needs. Therefore, to evaluate the real needs for young cancer patients comprehensively is the focus for future study (Yu et al., 2014). 5 Chapter2 Methodology 2.1 Chi-squaretest Having the data in hand, it is important to see how independent variables related to the dependent variables (response). The Chi-square test is the most popular method used to test the independence of 2 categorical variables. First, contingency table is obtained from dependent variables and the response variabl -es. The null hypothesis is: H 0 : two variables are independent. Later, the Chi-square statistic can be computed as: X 2 c = X (O i E i ) 2 E i (2.1) where c is the degree of freedom (c=(r-1)*(m-1), where r is the number of rows and m is the number if columns);O i is the observed value andE i is the expected value. 6 2.2 MultinomialLogisticRegression Similar to simple (binomial) logistic regression, multinomial logistic regre -ssion (MLR) is an extension which deals with the response that has more than two categories. In addition, in MLR, data will be aggregated into several populations, where each population has its unique parameter estimate. To illustrate the process behind the MLR modeling, assume a sampleX having S observations, K independent variables and one dependent variable having M categories, where M 3. Given a column vectorn, where element n i stands for the number of observations in population i, s.t. P N n=1 n i =S. For each population, let be a matrix of probabilities. Within it, each element ij represents the probability that the dependent variable falls into category j given the information of the independent variables in the ith population. Then, let be a vector of parameters, where each element kj stands for the parameter estimate of the kth covariate and jth value of the dependent variable. Later, a base category should be chosen in order to calculate the odds. In MLR any category can be used as the baseline. Usually the first or the last category will be chose to be the baseline category. Here, the last (Mth) category is chosen for illustration. Then, the odds of the jth observation compare to the Mth observation can be written as: logit( ij iM ) =ln( ij 1 P j=1 M1 ij ) = K X k=0 x ij kj (2.2) 7 where i = 1,2,...,N and j = 1,2,..., M-1. Then solving the above equations we will get: ij = e P K k=0 x ij kj 1 + P M1 j=1 e P K k=0 x ij kj (2.3) iM = 1 1 + P M1 j=1 e P K k=0 x ij kj (2.4) Now, in order to estimate the parameter of the independent variable, maxim -um likelihood estimates will be applied. In MLR, the response (dependent variables) follows the multinomial distribution with M levels. In each population, the joint PDF can be expressed as: f(yj) = N Y i=1 [ n i ! Q M j=1 y ij ! M Y j=1 y ij ij ] (2.5) Then, terms without ij can be ignored when the only interest is on maximizing the above equation with respect to. Thus, we get the kernel of the likelihood function as follows: L(jy) = N Y i=1 M Y j=1 y ij ij (2.6) Take the natural log of the likelihood function, we have: l() = N X i=1 M1 X j=1 (y ij K X k=0 x ij kj )n i log(1 + M1 X j=1 e P K k=0 x ij kj ) (2.7) Then the partial derivative will be computed. However, it cannot be solved in normal ways. In this case the Newton-Raphson iterative procedure will be applied. 8 2.3 Likelihoodratiotest The Likelihood Ratio (LR) test is known for comparing the difference between two models. It can help to decide whether a new model is better than a reduced model. To conduct the test, the maximum likelihood of new modelL 1 and reduced modelL 0 are computed. The null hypothesis is :H 0 : The full model fits the data better than the reduced model. Define the likelihood ratio as: =L 0 =L 1 (2.8) Then, -2ln() follows the Chi-square distribution with df equal to the parameter number difference between the full model and the reduced model. 2.4 Akaikeinformationcriterion The Akaike Information Criterion (AIC) is a common method used for comparing different models by evaluating how well a model fits the data. Using AIC as the model selection criterion, models that explain the greatest amount of variation while having the smallest number of variables in the model will be chosen as the best-fit model. The AIC is calculated from the maximum likelihood estimate of the model, and the number of independent variables used to build the model. Let M to be the number of independent variables used and L be the log-likelihood estimate for the model. Then the AIC can be expressed as: AIC = 2K 2ln(L) (2.9) 9 where K = M+2. Later,4 AIC, the difference of AIC between models, will be computed. The the rules below shall be followed to compare two models: •4 AIC< 2: there is substantial support for the model with higher AIC • 2<4 AIC< 4: there is substantial support for the model with higher AIC • 4<4 AIC< 7: there is considerably less support for the model with higher AIC • 10<4 AIC: there is essentially no support for the model with higher AIC 2.5 Bayesianinformationcriterion The Bayesian Information Criterion (BIC) is also a popular criterion for model selection in regression. It is calculated from likelihood function with a penalty term for number of parameters in the model. Let n to be the sample size and k to be the number of free parameters in the model. Denote the maximum likelihood function for the estimated model as L. Then BIC can be calculated as: BIC =kln(n) 2ln(L) (2.10) When comparing two models, we can compute4 BIC, which is the BIC difference between two models. Then, use the following rules to choose a better model: •4 BIC< 2: not enough evidence to show significant difference between 2 models • 2<4 BIC< 6: evidence for the best model and against the other model is positive • 6<4 BIC< 10: evidence for the best model and against the weaker model is strong 10 • 10<4 BIC: evidence favoring the best model is very strong 11 Chapter3 Results In this section, the results of different tests and models will be shown. All statistic analysis are conducted by STATA/SE (Version 15.1). The details of code can be seen in Appendix A. 3.1 Descriptivestatistics Characteristics about the patients are summarized in Table 3.1. Among 215 patients in this study, there are 102 (47.44%) from pediatric group and 113 (52.56%) from adult group. Within the sample, most patients are white (44.6%) and Hispanic (42.72%). There are only 16.43% of patients who have been through college education. In the pediatric group, patients are mostly of single relationship status (92%); while for adult group, they are mostly of married relationship status (65.77%). A large percents (68.34%) of the patients have household income less than $50,000. Basic statistics about the cancer information between patients in pediatric setting and adult setting are shown in Table 3.2. Dividing cancer type by 5-year survival rate, 29.30% of patients are 12 Table 3.1: Basic Characteristics of Patients Pediatric setting Adult care setting Total Characteristics No. % No. % No. % CurrentAge 14-19 102 100 0 0.00 102 47.44 20-29 0 0.00 48 42.48 48 22.33 30-39 0 0.00 65 57.52 65 30.23 Gender Male 60 58.82 54 47.79 114 53.02 Female 42 41.18 59 52.21 101 46.98 Race White/Caucasian 34 33.66 61 54.46 95 44.6 Black or African American 1 0.99 10 8.93 11 5.16 Asian/Pacific islander 11 10.89 2 1.79 13 6.10 Hispanic/Latino 55 54.46 36 32.14 91 42.72 American Indian 0 0.00 3 2.68 3 1.41 Education Grade School 20 19.80 4 3.57 24 11.27 Some High school 68 67.33 10 8.93 78 36.62 High school (12 years) 10 9.90 21 18.75 31 14.55 V ocational or training 3 2.97 37 33.04 40 18.78 Associate Degree 0 0.00 5 4.46 5 2.35 College Graduate 0 0.00 24 21.43 24 11.27 Post-graduate 0 0.00 11 9.82 11 5.16 Employment Full time 0 0.00 28 25.69 28 13.46 Part-time 1 1.01 10 9.17 11 5.29 Full-time homemaker 0 0.00 4 3.67 4 1.92 Full-time student 36 36.36 6 5.50 42 20.19 Temporary medical leave/ disability 16 16.16 30 27.52 46 22.12 Unemployed 32 32.32 27 24.77 59 28.37 Permanently unable to work 14 14.14 4 3.67 18 8.65 Relationship Single 92 92.00 38 34.23 130 61.61 Married 8 8.00 73 65.77 81 38.39 Householdincome $15,000 less 29 32.95 30 27.03 59 29.65 15; 00125,000 11 12.50 22 19.82 33 16.58 25; 00150,000 14 15.91 30 27.03 44 22.11 50; 00175,000 11 12.50 15 13.51 26 13.07 75; 001100,000 4 4.55 7 6.31 11 5.53 $100,000 more 9 10.23 7 6.31 16 8.04 Do not know 10 11.36 0 0.00 10 5.03 13 Table 3.2: Cancer information of Patients Pediatric setting Adult care setting Total Characteristics No. % No. % No. % Cancertypebysurvivalrate <50% 5-year survival rate 39 38.24 24 21.24 63 29.30 50%-80% 5-year survival rate 43 42.16 44 38.94 87 40.47 >80% 5-year survival rate 20 19.61 45 39.82 65 30.23 Overalltreatment Radiation Received 15 14.71 22 19.64 37 17.29 Not received 87 85.29 90 80.36 177 82.71 Chemotherapy Received 97 95.10 78 69.64 175 81.78 Not received 5 4.90 34 30.36 39 18.22 Surgery Received 41 40.20 25 22.32 66 30.84 Not received 61 59.80 87 77.68 148 69.16 of the cancer type having 5-year survival rate less than 50%, 40.47% pf patients are of the cancer type having 5-year survival rate between 50% and 80%; 30.23% of patients are of the cancer type having 5-year survival rate greater than 80%. Most patients have not received radiation (82.71%) and surgery (69.16%). A large percents of the patients have received chemotherapy (81.78%). In later analysis, categories that are infrequent will be combined to reduce the variable classes. Specifically, categories that have frequencies less than or equal to 30 will be combined. Race will recategorized intofWhite, Hispanic and othersg; Education will be recategorized intof<High school, High school/ General education and Collegeg; Employment will be recategorized into fEmployed and Not employedg ; Income will be recategorized intof $15,000, $15,001-$50,000 and othersg. 14 3.2 Chi-squaretestandUnivariateAnalysis After assigning new categories to variables, Chi-square tests of the relation -ship between response variables and each potential predicators are conducted (Table 3.3). In this study, the significant level is set less than or equal to 0.05. Income is significantly related to services that offer information about illness, treatment, risks for recurrence, or second cancers (p-value = .019). Age, gender, education and race are significantly related to services that offer cancer education or support appropriate for adolescents and young adults online (p-value<.001, =.050, =.019 respectively). Ages are significantly related to services that offer information about diet and nutrition (p-value = .006). Age, gender and education are significantly related to services that offer cancer education or support appropriate for adolescents and young adults in community centers, camps, retreats, or adventure programs (p-value =.007, .038, .031 respectively). Gender, education and surgery are significantly related to services that offer counseling by mental health professionals (p-value =.001, .031, .051 respectively). Age, education and chemotherapy are significantly associated to services that offer guidance related to sexuality or intimacy (p-value =.002, =.002, <.001 respectively). Age, gender, education, race, relationship and surgery are significantly associated with services that help with understanding health insurance, disability, or social security (p-value<.001, =.041, =.033, =.001, =.011, =.040 respectively). Age and education are significantly associat -ed with services that provide infertility treatment (p-value =.002, .004 respecti -vely). Education and employment are significantly associated with services that give transportation assistance (p-value =.013, .011 respectively). 15 Table 3.3: Chi-square test of potential predictors and response variables Cancer info Internet site Diet info Community Mental health Sexuality Insurance Infertility Transportation Variables Age 9.082(.059) 24.552(.000) 14.611(.006) 14.186(.007) 6.333(.176) 16.789(.002) 31.075(.000) 16.928(.002) 7.388(.117) Gender 4.804(.091) 5.994(.050) 0.566(.754) 6.543(.038) 13.669(.001) 2.021(.364) 6.396(.041) 2.873(.238) 3.0623(.216) Education 8.106(.088) 11.841(.019) 6.260(.181) 21.446(.000) 10.627(.031) 17.029(.002) 10.479(.033) 15.596(.004) 12.657(.013) Race 7.259(.123) 12.283(.015) 1.218(0.875) 6.332(.176) 3.218(.522) 3.048(.550) 18.048(.001) 2.317(.678) 3.415(.491) Employment 2.998(.223) 1.471(.479) 0.578(.749) 0.753(.686) 0.122(.941) 0.336(.845) 0.187(.911) 2.596(.273) 9.0582(.011) Income 11.809(.019) 2.239(.692) 7.563(.109) 7.067(.132) 2.229(.694) 2.579(.631) 0.928(.921) 4.132(.388) 4.332(.363) Relationship 2.330(.312) 0.811(.667) 4.664(.097) 1.536(.464) 2.398(.302) 2.196(.334) 8.988(.011) 4.950(.084) 2.090(.352) Cancer type 2.736(.603) 7.945(.094) 7.016(.135) 2.745(.601) 4.076(.396) 3.078(.545) 6.572(.160) 2.703(.609) 2.861(.581) Radiation 1.430(.489) 0.430(.807) 1.897(.387) 0.526(.769) 0.040(.980) 3.683(.159) 1.367(.505) 2.110(.348) 0.335(.846) Chemotherapy 5.936(.051) 5.115(.077) 4.996(.082) 2.356(.308) 0.467(.792) 19.547(.000) 4.117(.128) 3.374(.185) 0.839(.657) Surgery 1.518(.468) 1.139(.566) 4.356(.113) 5.944(.051) 7.442(.024) 3.190(.203) 6.428(.040) 0.463(.793) 1.869(.393) Note: 9.082(.059) means Pearson Chi-square and its p-value. 16 Table 3.4: Potential predictors for each response variable Cancer info Internet site Diet info Community Mental health Sexuality Insurance Infertility Transportation Variables Age * * * * * * * * * Gender * * * * * * * * Education * * * * * * * * Race * * * * * Employment * Income * * * * * * Relationship * * * * * * * Cancer type * * * * * * * Radiation * * * Chemotherapy * * * * * Surgery * * * * * * Note: * stands for important univariate p-value (>0.25). Although, from the results of chi-square test, not all 9 chosen response variables are significantly associated with treatments (including radiation, surge -ry and chemotherapy), these three variables will still be put in the preliminary model because of their clinical importance. Table 3.4 shows variables having important univariate p-values (p-values less than 0.1) for each response variables. 3.3 MultivariableMLRmodelbuilding To assess the joint effects of these predictor variables, a multivariable MLR model is developed for each of these response variable with all the potential covariates listed in Table 3.4. The final model was determined through the stepwise procedure based on AIC and BIC model selection criteria (Table 3.5.). 3.4 InterpretationsofMLRmodels Odds ratios and 95% confidence intervals for predictors in each MLR models for unmet services are listed in Table 3.6. Patients ages from 30 to 39 were more likely to report unmet services that offer cancer education or support appropriate for adolescents and young adults online 17 Table 3.5: AIC and BIC for potential models Model1 (Preliminary) Model2 (Stepwise) Final model AIC BIC ACI BIC Response Cancer 337.465 397.799 339.315 399.563 model 1 Internet 445.953 533.468 441.715 495.571 model 2 Diet 348.915 435.940 340.484 400.732 model 2 Community 243.942 317.994 246.295 300.151 model 2 Mental 261.075 328.018 263.424 316.977 model 2 Sexuality 457.815 551.534 446.551 506.799 model 2 Insurance 375.930 469.649 370.483 444.119 model 2 Infertity 461.204 554.923 455.032 515.280 model 2 Transportation 274.121 347.757 270.019 323.573 model 2 Abbreviation: AIC, Akaika information criterion; BIC, Bayesian information criterion. (OR, 2.66; 95% CI, 1.08 to 6.55), compared to patients of other ages. Patients with cancer having 5-year survival rate>80% were more likely to report unmet services that offer cancer education or support appropriate for adolescents and young adults online (OR, 3.27; 95% CI, 1.21 to 8.82), compared to patients having cancer of other 5-year survival rates. Patients that are at college- educated level were more likely to report unmet services that offer cancer education or support appropriate for adolescents and young adults in community centers, camps, retreats, or adventure programs (OR, 3.67; 95% CI, 1.28 to 10.52), compared to patients of other education levels. Male patients were less likely to report unmet services that offer counseling by mental health professionals (OR, 0.33; 95% CI, 0.14 to 0.79), compared to female patients. Patients who have received surgeries were less likely to report unmet services that offer counseling by mental health professionals (OR, 0.30; 95% CI, 0.09 to 0.98), compared to patient not s who have not received surgeries. Patients who have received chemotherapy were less likely to report unmet services that offer guidance related to sexuality or intimacy (OR, 0.35; 95% CI, 0.14 to 0.88), compared to 18 patients who have not received chemotherapy. Patients ages from 30 to 39 were more likely to report unmet services that help with understanding health insurance, disability, or social security (OR, 5.56; 95% CI, 1.79 to 17.27), compared to patients of other ages. Patients ages from 30 to 39 were more likely to report unmet services that provide infertility treatment (OR, 4.55; 95% CI, 1.62 to 12.79), compared to patients of other age. Patients that are employed or in school were less likely to report unmet services that give transportation assistance (OR, 0.30; 95% CI, 0.10 to 0.93), compared to patients that are not employed or not in school. Patients with cancer having 5- year survival rate>80% were less likely to report unmet services that give transportation assistance (OR, 0.35; 95% CI, 0.57 to 3.15), compared to patients having cancer of other 5-year survival rates. Odds ratios and 95% confidence intervals for predictors in each MLR models for used services are listed in Table 3.7. Patients who ages from 20 to 29 were less likely to report used services that offer cancer education or support appropriate for adolescents and young adults online (OR, 0.23; 95% CI, 0.07 to 0.73), compared to patients of other ages. Patients who ages from 20 to 29 were less likely to report used service that offer information about diet and nutrition(OR, 0.08; 95% CI, 0.01 to 0.72), compared to patients of other ages. Patients with cancer having 5-year survival rate>80% were more likely to report used services that offer information about diet and nutrition (OR, 4.45; 95% CI, 1.26 to 15.73), compared to patients having cancer of other 5-year survival rates. Patients who have received surgeries were less likely to report used services that offer cancer education or support appropriate for adolescents and young adults in community centers, camps, retreats, or adventure programs (OR, 0.09; 95% CI, 0.01 to 0.82), compared to patients who have not received surgeries. Patients that are at high school-educated level were less likely to report used services that offer guidance related to sexuality or intimacy (OR, 0.35; 95% CI, 0.15 to 0.79), 19 Table 3.6: Models of Unmet Service Cancer info Internet site Diet info Community Mental health Sexuality Insurance Infertility Transportation Predictors [OR (CI)] Age (20-29) 7.09(.93, 53.25) 2.10(.85, 5.20) 1.41(.51, 3.91) 1.54(.49, 4.80) 4.81(1.82, 12.66) * Age (30-39) 4.13(.62, 28.24) 2.66(1.08, 6.55) * .56(.16, 1.35) 5.56(1.79, 17.27) * 4.55(1.62, 12.79) * Gender (Male) 1.08(.33, 3.47) .33(.14, .79) * Education (High school) .59(.12, 2.83) 1.29(.47, 3.52) .82(.37, 1.79) Education (College) 1.02(.06, 15.89) 3.67(1.28, 10.52) * 2.53(.84, 7.64) Race (White) .43(.15, 1.21) Race (Hispanic) .96(.33, 2.77) Employment (employed) .30(.10, .93) * Income (<$15,0000) 2.03(.52, 7.97) Income (15; 00025000) 1.14(.27, 4.83) Relationship (Married) 1.19(.28, 5.11) 1.54(.61, 3.86) 1.39(.60, 3.21) 1.62(.68, 3.88) .68(.29, 1.58) 1.78(.80, 4.50) Cancer type (50%-80%) 2.52(.96, 6.57) .63(.24, 1.66) .94(.32, 2.80) .95(.31, 2.90) .77(.31, 1.91) .63(.25, 1.64) 1.34(.57, 3.14) 1.65(.44, 6.12) Cancer type (>80%) 3.27(1.21, 8.82) * 1.06(.38, 2.95) 1.29(.46, 3.62) 1.51(.51, 4.43) 1.24(.47, 3.26) .84(.32, 2.20) 1.24(.50, 3.07) 3.47(.95, 12.69) * Radiation (Received) 1.05(.43, 2.58) .77(.26, 2.28) 1.36(.48, 3.90) .66(.21, 2.07) 1.60(.54, 4.70) .74(.28, 1.98) 1.12(.48, 2.59) .47(.12, 1.86) Chemotherapy (Received) 1.72(.72, 4.11) 1.12(.39, 3.24) .92(.33, 2.59) .61(.21, 1.74) .35(.14, .88) * 1.65(.61, 4.49) 1.06(.44, 2.52) .57(.19, 1.69) Surgery (Received) .73(.32, 1.64) .50(.19, 1.35) .47(.16, 1.38) .30(.09, .98) * 1.34(.57, 3.15) .59(.24, 1.47) .98(.46, 2.11) .88(.31, 2.46) Note: * stands for p-value less than 0.05. Abbreviation: OR, odds ratio; CI, 95 % confidence interval. 20 compared to patients of other education levels. Patients with cancer having 5-year survival rate in the interval [50%-80%] were more likely to report used services that help with understanding health insurance, disability, or social security (OR, 4.43; 95% CI, 1.21 to 16.22), compared to patients having cancer of other 5-year survival rates. Patients who have received chemotherapy were more likely to report used services that help with understanding health insurance, disability, or social security (OR, 9.59; 95% CI, 1.60 to 57.51), compared to patient who have not received chemotherapy. Patients that are employed or in school were more likely to report unmet services that give transportation assistance (OR, 4.04; 95% CI, 1.32 to 12.37), compared to patients that are not employed or not in school. 21 Table 3.7: Models of Used Service Cancer info Internet Diet info Community Mental health Sexuality Insurance Infertility Transportation Predictors[OR(CI)] Age (20-29) 1.21(.20, 7.26) .23(.07, .73) * .08(.01, .72) * .12(.01, 1.20) 2.39(.79, 7.27) Age (30-39) .77(.14, 4.17) .56(.22, 1.43) .26(.06, 1.18) 1.05(.23, 4.76) 1.89(.57, 6.29) Gender (Male) .48(.18, 1.31) .26(.06, 1.05) Education (High school) .87(.23, 3.38) .35(.15, .79)* Education (College) 3.72(.31, 44.25) .89(.27, 2.96) Race (White) .53(.09, 3.15) Race (Hispanic) 3.79(.72, 20.00) Employment (employed) 4.04(1.32, 12.37) * Income (<$15,0000) 0.76(.24, 2.37) Income (15; 00025000) 1.36(.41, 4.42) Relationship (Married) 1.21(.32, 4.55) .52(.14, 1.95) 1.28(.35, 4.69) 2.74(.74, 10.37) 1.38(.52, 3.71) .72(.23, 2.24) Cancer type (50%-80%) 1.44(.58, 3.54) .78(.23, 2.63) 2.12(.44, 10.31) .92(.19, 4.40) .89(.35, 2.24) 4.43(1.21, 16.22) * .85(.32, 2,25) 1.28(.32, 5.08) Cancer type (>80%) 1.89(.71, 5.04) 4.45(1.26, 15.73) * 3.14(.41, 23.91) .72(.13, 3.87) 1.20(.43, 3.33) 4.25(1.06, 17.11) * .97(.35, 2.68) 1.44(.36, 5.85) Radiation (Received) 1.09(.43, 2.77) .50(.13, 1.97) .33(.04, 3.02) .77(.14, 4.15) 2.85(.98, 8.29) 1.35(.46, 3.97) .68(.22, 2.09) 1.66(.47, 5.93) Chemotherapy (Received) 2.64(.79, 8.85) 3.56(.40, 32.01) .75(.13, 4.18) 2.98(.81, 11.01) 9.59(1.60, 57.51) * 1.92(.59, 6.24) 1.16(.28, 4.78) Surgery (Received) .89(.39, 2.02) .30(.09, 1.02) .09(.01, .82) * .20(.02, 1.76) 1.98(.83, 4.72) .63(.22, 1.75) .99(.40, 2.45) .41(.10, 1.66) Note: * stands for p-value less than 0.05. Abbreviation: OR, odds ratio; CI, 95 % confidence interval. 22 Chapter4 DiscussionandConclusion 4.1 Discussion In case patients lack the knowledge of protecting themselves scientifically, services that offer informational resources for young cancer survivors need to be enhanced for more patients to access. Those services are information about cancer education, cancer risk, treatment and diet. To help patients learn how to nurse themselves correctly, services that provide professional guidance for young cancer survivors need to be put more into practice so that each patient can get helpful advices. Those services include: psychological services, sexual services and supportive treatments. In addition, practical support services that related to insurance, infertility and transportation should be paid more attention to so that patients have more opportunity to get access to those services. In this study, cross validation is eliminated due to small sample size. If validation is used, an even smaller sample will be applied to build predication models, which might cause lost information about the whole sample. Therefore, a larger sample should be collected for future analysis. In addition, when using logic regression for category, a balanced dataset is needed to have a better 23 performance. Thus, balancing techniques like over sampling and under sampling will be preferable for further study. 4.2 Conclusion All kinds of service are unmet for adolescent and young adult cancer to some extent. It is important to enhance specific kinds of unmet service for specific group of patients. That way, services can be targeted to the right patients and the use of all kinds of services can be maximized. For future analysis, larger sample size and optimized model building are needed to have a better understanding of the unmet needs for adolescents and young cancer survivors. 24 Bibliography [1] Fact sheets. (2018, September 12). Retrieved March 02, 2021, from https://www.who.int/news-room/fact-sheets [2] Adolescents and young adults (ayas) with cancer. (2020, September 24). Retrieved March 02, 2021, from https://www.cancer.gov/types/aya [3] Teenage cancer statistics: Adolescent cancer stats. (2021, January 21). Retrieved March 02, 2021, from https://www.cancer.org/cancer/cancer-in-adolescents/key-statistics.html [4] Treating cancers in young adults. (2019, October 24). Retrieved March 02, 2021, from https://www.cancer.org/cancer/cancer-in-young-adults/treating-cancers-in-young- adults.html [5] Carey, M. L., Clinton-McHarg, T., Sanson-Fisher, R. W., & Shakeshaft, A. (2012). Development of cancer needs questionnaire for parents and carers of adolescents and young adults with cancer. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 20(5), 991–1010. https://doi.org/10.1007/s00520- 011-1172-2 25 [6] McCarthy, M. C., McNeil, R., Drew, S., Orme, L., & Sawyer, S. M. (2018). Information needs of adolescent and young adult cancer patients and their parent-carers. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 26(5), 1655–1664. https://doi.org/10.1007/s00520-017-3984-1 [7] Jiang, J., & Xia, H. (2009). Study on information needs of cancer patients and nursing support strategy. Shanghai Nursing, 9(2). https://www.ixueshu.com/document/a8e5471c956a5e97318947a18e7f93 86.html [8] Patterson, P., McDonald, F. E., Zebrack, B., & Medlow, S. (2015). Emerging issues among adolescent and young adult cancer survivors. Seminars in oncology nursing, 31(1), 53–59. https://doi.org/10.1016/j.soncn.2014.11.006 [9] Whitney, R. L., Bell, J. F., Bold, R. J., & Joseph, J. G. (2015). Mental health needs and service use in a national sample of adult cancer survivors in the USA: has psychosocial care improved?. Psycho-oncology, 24(1), 80–88. https://doi.org/10.1002/pon.3569 [10] Zebrack B. (2009). Information and service needs for young adult cancer survivors. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 17(4), 349–357. https://doi.org/10.1007/s00520-008-0469-2 [11] Lambert, S. D., Harrison, J. D., Smilth, E., Bonenvki, B., Carey, M., Lawsin, C., . . . Girgis, A. (2012). The unmet needs of partners and caregivers of adults diagnosed with cancer: A systematic review. BMJ Supportive & Palliative Care, 2(3), 224-230. doi:10.1136/bmjspcare- 2012-000226 26 [12] Wang, T., Molassiotis, A., Chung, B. P., & Tan, J. (2018). Unmet care needs of advanced cancer patients and their informal caregivers: A systematic review. BMI Palliative Care, 17(96). doi:10.1186/s12904-018-0346-9 [13] Yu, F., Fu, J., Bai, Y ., Zhu, L., Zhang, R., Jiao, H., & Zhang, Y . (2014). Current status of research on supportive care needs of adult cancer patients. People’s Liberation Army Nursing Journal, 31(19). doi: 10.3969/jissn.1008-9993/2014.19.007 27 Appendix AppendixA:STATACode 1 2 use ” / Users / s h e r r y s h e n / Desktop / c a n c e r . d t a ” 3 4 ************************************* B a s i c d i s c r i p t i v e s t a t i s t i c s 5 * s p l i t d a t a i n t o p e d i a t r i c and a d u l t 6 gen ped =0 7 r e p l a c e ped =1 i f age>19.9 8 9 ****************** compute f r e q u e n c i e s 10 gen a g e c a t =0 11 r e p l a c e a g e c a t =1 i f age>19.9 & age<30 12 r e p l a c e a g e c a t =2 i f age>29.9 13 t a b a g e c a t ped , c e l l column 14 t a b g en d er ped , c e l l column 15 t a b r a c e ped , c e l l column 16 t a b edu ped , c e l l column 17 t a b emp ped , c e l l column 18 t a b r e l a t ped , c e l l column 19 t a b income ped , c e l l column 20 t a b c n a c e r ped , c e l l column 21 t a b r a d i a t i o n ped , c e l l column 22 t a b chemo ped , c e l l column 23 t a b s u r g e r y ped , c e l l column 24 25 26 ***** change r e s p o n s e i n t o 3 c a t e g o r i e s ( 0 : s e r v i c e used 1 : s e r v i c e unmet 2 : o t h e r s ) 27 ***** I choose 9 q u e s t i o n form t h e q u e s t i o n n a i r e as 9 r e s p o n s e s t o do MLR a n a l y z e 28 gen i l l =0 29 r e p l a c e i l l =1 i f c a n i n f o ==3 28 30 r e p l a c e i l l =2 i f c a n i n f o ==4 31 gen n e t =0 32 r e p l a c e n e t =1 i f i n t e r i n f o ==3 33 r e p l a c e n e t =2 i f i n t e r i n f o ==4 34 gen d i e t =0 35 r e p l a c e d i e t =1 i f d i e t i n f o ==3 36 r e p l a c e d i e t =2 i f d i e t i n f o ==4 37 gen commu=0 38 r e p l a c e commu=1 i f com emo==3 39 r e p l a c e commu=2 i f com emo==4 40 gen m ent al =0 41 r e p l a c e m ent al =1 i f mental emo ==3 42 r e p l a c e m ent al =2 i f mental emo ==4 43 gen sex =0 44 r e p l a c e sex =1 i f sex emo ==3 45 r e p l a c e sex =2 i f sex emo ==4 46 gen i n s u r =0 47 r e p l a c e i n s u r =1 i f i n s p r a c ==3 48 r e p l a c e i n s u r =2 i f i n s p r a c ==4 49 gen i n f e r t =0 50 r e p l a c e i n f e r t =1 i f i n f e r t p r a c ==3 51 r e p l a c e i n f e r t =2 i f i n f e r t p r a c ==4 52 gen t r a n s =0 53 r e p l a c e t r a n s =1 i f t r a n s p p r a c ==3 54 r e p l a c e t r a n s =2 i f t r a n s p p r a c ==4 55 56 57 ****** a s s i g n new c a t e g o r y t o race , edu , emp , income 58 gen r a c e c a t =0 59 r e p l a c e r a c e c a t =1 i f r a c e ==1 60 r e p l a c e r a c e c a t =2 i f r a c e ==4 61 gen e d u c a t =0 62 r e p l a c e e d u c a t =1 i f edu ==3 63 r e p l a c e e d u c a t =1 i f edu ==4 64 r e p l a c e e d u c a t =1 i f edu ==5 65 r e p l a c e e d u c a t =2 i f edu ==6 66 r e p l a c e e d u c a t =2 i f edu ==7 67 gen empcat =0 68 r e p l a c e empcat =1 i f emp==1 69 r e p l a c e empcat =1 i f emp==2 70 r e p l a c e empcat =1 i f emp==3 71 r e p l a c e empcat =1 i f emp==4 72 gen i n c o m e c a t =0 73 r e p l a c e i n c o m e c a t =1 i f income ==1 74 r e p l a c e i n c o m e c a t =2 i f income ==2 75 r e p l a c e i n c o m e c a t =2 i f income ==3 29 76 77 78 ******************* c h i 2 t e s t 79 t a b i l l a g e c a t , row c h i 2 80 t a b i l l gender , row c h i 2 81 t a b i l l educat , row c h i 2 82 t a b i l l r a c e c a t , row c h i 2 83 t a b i l l empcat , row c h i 2 84 t a b i l l incomecat , row c h i 2 85 t a b i l l r e l a t , row c h i 2 86 t a b i l l cancer , row c h i 2 87 t a b i l l r a d i a t i o n , row c h i 2 88 t a b i l l chemo , row c h i 2 89 t a b i l l s u r g e r y , row c h i 2 90 91 t a b n e t a g e c a t , row c h i 2 92 t a b n e t gender , row c h i 2 93 t a b n e t educat , row c h i 2 94 t a b n e t r a c e c a t , row c h i 2 95 t a b n e t empcat , row c h i 2 96 t a b n e t incomecat , row c h i 2 97 t a b n e t r e l a t , row c h i 2 98 t a b n e t cancer , row c h i 2 99 t a b n e t r a d i a t i o n , row c h i 2 100 t a b n e t chemo , row c h i 2 101 t a b n e t s u r g e r y , row c h i 2 102 103 t a b d i e t a g e c a t , row c h i 2 104 t a b d i e t gender , row c h i 2 105 t a b d i e t educat , row c h i 2 106 t a b d i e t r a c e c a t , row c h i 2 107 t a b d i e t empcat , row c h i 2 108 t a b d i e t incomecat , row c h i 2 109 t a b d i e t r e l a t , row c h i 2 110 t a b d i e t cancer , row c h i 2 111 t a b d i e t r a d i a t i o n , row c h i 2 112 t a b d i e t chemo , row c h i 2 113 t a b d i e t s u r g e r y , row c h i 2 114 115 t a b commu a g e c a t , row c h i 2 116 t a b commu gender , row c h i 2 117 t a b commu educat , row c h i 2 118 t a b commu r a c e c a t , row c h i 2 119 t a b commu empcat , row c h i 2 120 t a b commu incomecat , row c h i 2 121 t a b commu r e l a t , row c h i 2 30 122 t a b commu cancer , row c h i 2 123 t a b commu r a d i a t i o n , row c h i 2 124 t a b commu chemo , row c h i 2 125 t a b commu s u r g e r y , row c h i 2 126 127 t a b m ent al a g e c a t , row c h i 2 128 t a b m ent al gender , row c h i 2 129 t a b m ent al educat , row c h i 2 130 t a b m ent al r a c e c a t , row c h i 2 131 t a b m ent al empcat , row c h i 2 132 t a b m ent al incomecat , row c h i 2 133 t a b m ent al r e l a t , row c h i 2 134 t a b m ent al cancer , row c h i 2 135 t a b m ent al r a d i a t i o n , row c h i 2 136 t a b m ent al chemo , row c h i 2 137 t a b m ent al s u r g e r y , row c h i 2 138 139 t a b sex a g e c a t , row c h i 2 140 t a b sex gender , row c h i 2 141 t a b sex educat , row c h i 2 142 t a b sex r a c e c a t , row c h i 2 143 t a b sex empcat , row c h i 2 144 t a b sex incomecat , row c h i 2 145 t a b sex r e l a t , row c h i 2 146 t a b sex cancer , row c h i 2 147 t a b sex r a d i a t i o n , row c h i 2 148 t a b sex chemo , row c h i 2 149 t a b sex s u r g e r y , row c h i 2 150 151 t a b i n s u r a g e c a t , row c h i 2 152 t a b i n s u r gender , row c h i 2 153 t a b i n s u r educat , row c h i 2 154 t a b i n s u r r a c e c a t , row c h i 2 155 t a b i n s u r empcat , row c h i 2 156 t a b i n s u r incomecat , row c h i 2 157 t a b i n s u r r e l a t , row c h i 2 158 t a b i n s u r cancer , row c h i 2 159 t a b i n s u r r a d i a t i o n , row c h i 2 160 t a b i n s u r chemo , row c h i 2 161 t a b i n s u r s u r g e r y , row c h i 2 162 163 t a b i n f e r t a g e c a t , row c h i 2 164 t a b i n f e r t gender , row c h i 2 165 t a b i n f e r t educat , row c h i 2 166 t a b i n f e r t r a c e c a t , row c h i 2 167 t a b i n f e r t empcat , row c h i 2 31 168 t a b i n f e r t incomecat , row c h i 2 169 t a b i n f e r t r e l a t , row c h i 2 170 t a b i n f e r t cancer , row c h i 2 171 t a b i n f e r t r a d i a t i o n , row c h i 2 172 t a b i n f e r t chemo , row c h i 2 173 t a b i n f e r t s u r g e r y , row c h i 2 174 175 t a b t r a n s a g e c a t , row c h i 2 176 t a b t r a n s gender , row c h i 2 177 t a b t r a n s educat , row c h i 2 178 t a b t r a n s r a c e c a t , row c h i 2 179 t a b t r a n s empcat , row c h i 2 180 t a b t r a n s incomecat , row c h i 2 181 t a b t r a n s r e l a t , row c h i 2 182 t a b t r a n s cancer , row c h i 2 183 t a b t r a n s r a d i a t i o n , row c h i 2 184 t a b t r a n s chemo , row c h i 2 185 t a b t r a n s s u r g e r y , row c h i 2 186 187 188 * u n i v a r a i a t e l o g i s t i c r e g r e s s i o n 189 m l o g i t i l l i . a g e c a t , r r r b ase ( 2 ) 190 m l o g i t i l l i . gender , r r r base ( 2 ) 191 m l o g i t i l l i . educat , r r r b ase ( 2 ) 192 m l o g i t i l l i . r a c e c a t , r r r b ase ( 2 ) 193 m l o g i t i l l i . empcat , r r r base ( 2 ) 194 m l o g i t i l l i . incomecat , r r r base ( 2 ) 195 m l o g i t i l l i . r e l a t , r r r base ( 2 ) 196 m l o g i t i l l i . cancer , r r r b ase ( 2 ) 197 m l o g i t i l l i . r a d i a t i o n , r r r base ( 2 ) 198 m l o g i t i l l i . chemo , r r r base ( 2 ) 199 m l o g i t i l l i . s u r g e r y , r r r b ase ( 2 ) 200 201 m l o g i t n e t i . a g e c a t , r r r base ( 2 ) 202 m l o g i t n e t i . gender , r r r base ( 2 ) 203 m l o g i t n e t i . educat , r r r b ase ( 2 ) 204 m l o g i t n e t i . r a c e c a t , r r r b ase ( 2 ) 205 m l o g i t n e t i . empcat , r r r base ( 2 ) 206 m l o g i t n e t i . incomecat , r r r base ( 2 ) 207 m l o g i t n e t i . r e l a t , r r r base ( 2 ) 208 m l o g i t n e t i . cancer , r r r base ( 2 ) 209 m l o g i t n e t i . r a d i a t i o n , r r r base ( 2 ) 210 m l o g i t n e t i . chemo , r r r base ( 2 ) 211 m l o g i t n e t i . s u r g e r y , r r r base ( 2 ) 212 213 m l o g i t d i e t i . a g e c a t , r r r b ase ( 2 ) 32 214 m l o g i t d i e t i . gender , r r r base ( 2 ) 215 m l o g i t d i e t i . educat , r r r base ( 2 ) 216 m l o g i t d i e t i . r a c e c a t , r r r b ase ( 2 ) 217 m l o g i t d i e t i . empcat , r r r b ase ( 2 ) 218 m l o g i t d i e t i . incomecat , r r r b ase ( 2 ) 219 m l o g i t d i e t i . r e l a t , r r r base ( 2 ) 220 m l o g i t d i e t i . cancer , r r r base ( 2 ) 221 m l o g i t d i e t i . r a d i a t i o n , r r r base ( 2 ) 222 m l o g i t d i e t i . chemo , r r r b ase ( 2 ) 223 m l o g i t d i e t i . s u r g e r y , r r r b ase ( 2 ) 224 225 m l o g i t commu i . a g e c a t , r r r b ase ( 2 ) 226 m l o g i t commu i . gender , r r r base ( 2 ) 227 m l o g i t commu i . educat , r r r b ase ( 2 ) 228 m l o g i t commu i . r a c e c a t , r r r base ( 2 ) 229 m l o g i t commu i . empcat , r r r b ase ( 2 ) 230 m l o g i t commu i . incomecat , r r r base ( 2 ) 231 m l o g i t commu i . r e l a t , r r r b ase ( 2 ) 232 m l o g i t commu i . cancer , r r r b ase ( 2 ) 233 m l o g i t commu i . r a d i a t i o n , r r r base ( 2 ) 234 m l o g i t commu i . chemo , r r r b ase ( 2 ) 235 m l o g i t commu i . s u r g e r y , r r r base ( 2 ) 236 237 m l o g i t m ent al i . a g e c a t , r r r b ase ( 2 ) 238 m l o g i t m ent al i . gender , r r r base ( 2 ) 239 m l o g i t m ent al i . educat , r r r base ( 2 ) 240 m l o g i t m ent al i . r a c e c a t , r r r b ase ( 2 ) 241 m l o g i t m ent al i . empcat , r r r b ase ( 2 ) 242 m l o g i t m ent al i . incomecat , r r r base ( 2 ) 243 m l o g i t m ent al i . r e l a t , r r r b ase ( 2 ) 244 m l o g i t m ent al i . cancer , r r r base ( 2 ) 245 m l o g i t m ent al i . r a d i a t i o n , r r r base ( 2 ) 246 m l o g i t m ent al i . chemo , r r r b ase ( 2 ) 247 m l o g i t m ent al i . s u r g e r y , r r r base ( 2 ) 248 249 m l o g i t sex i . a g e c a t , r r r b ase ( 2 ) 250 m l o g i t sex i . gender , r r r base ( 2 ) 251 m l o g i t sex i . educat , r r r b ase ( 2 ) 252 m l o g i t sex i . r a c e c a t , r r r b ase ( 2 ) 253 m l o g i t sex i . empcat , r r r base ( 2 ) 254 m l o g i t sex i . incomecat , r r r base ( 2 ) 255 m l o g i t sex i . r e l a t , r r r base ( 2 ) 256 m l o g i t sex i . cancer , r r r b ase ( 2 ) 257 m 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a t i c 867 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat 868 e s t a t i c 869 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r e l a t 870 e s t a t i c 871 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . ge nd er 872 e s t a t i c 873 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . i n c o m e c a t 874 e s t a t i c 875 876 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . empcat 877 e s t a t i c 878 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . r e l a t 879 e s t a t i c 880 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . ge nd er 881 e s t a t i c 882 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . i n c o m e c a t 883 e s t a t i c 884 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat i . r e l a t 270.0194 323.5731 885 e s t a t i c 886 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat i . ge nd er 887 e s t a t i c 888 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat i . i n c o m e c a t 889 e s t a t i c 890 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r e l a t i . ge nd er 891 e s t a t i c 892 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r e l a t i . i n c o m e c a t 893 e s t a t i c 894 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . ge nd er i . i n c o m e c a t 895 e s t a t i c 896 897 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . ge nd er i . r e l a t i . i n c o m e c a t 898 e s t a t i c 899 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat i . ge nd er i . i n c o m e c a t 900 e s t a t i c 901 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r e l a t i . empcat i . i n c o m e c a t 902 e s t a t i c 903 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat i . ge nd er i . r e l a t 47 904 e s t a t i c 905 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . ge nd er i . i n c o m e c a t i . a g e c a t 906 e s t a t i c 907 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r e l a t i . i n c o m e c a t i . a g e c a t 908 e s t a t i c 909 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . ge nd er i . r e l a t i . a g e c a t 910 e s t a t i c 911 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . i n c o m e c a t i . a g e c a t i . empcat 912 e s t a t i c 913 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . empcat i . ge nd e r 914 e s t a t i c 915 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . empcat i . r e l a t 916 e s t a t i c 917 918 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . i n c o m e c a t i . empcat i . ge nd e r i . r e l a t 919 e s t a t i c 920 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . i n c o m e c a t i . a g e c a t i . ge nd e r i . r e l a t 921 e s t a t i c 922 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . i n c o m e c a t i . a g e c a t i . empcat i . ge nd e r 923 e s t a t i c 924 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . i n c o m e c a t i . a g e c a t i . empcat i . r e l a t 925 e s t a t i c 926 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . empcat i . ge nd e r i . r e l a t 927 e s t a t i c 928 929 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . empcat i . ge nd e r i . r e l a t i . i n c o m e c a t 930 e s t a t i c 931 932 ***************************************** F i n a l 9 models : 933 m l o g i t t r a n s i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . empcat i . r e l a t , r r r base ( 2 ) 934 m l o g i t i n f e r t i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . r e l a t , r r r base ( 2 ) 935 m l o g i t i n s u r i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r a c e c a t i . r e l a t i . a g e c a t , r r r base ( 2 ) 936 m l o g i t sex i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . educat , r r r base ( 2 ) 937 m l o g i t m ent al i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . r e l a t i . gender , r r r base ( 2 ) 938 m l o g i t commu i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . educat , r r r base ( 2 ) 939 m l o g i t d i e t i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t i . r e l a t , r r r base ( 2 ) 940 m l o g i t n e t i . c a n c e r i . r a d i a t i o n i . chemo i . s u r g e r y i . a g e c a t , r r r base ( 2 ) 941 m l o g i t i l l i . a g e c a t i . e d u c a t i . g e nd er i . i n c o m e c a t i . r e l a t , r r r base ( 2 ) 48
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Asset Metadata
Creator
Shen, ShiYu (Shiyu)
(author)
Core Title
Analysis of unmet needs for young cancer survivors using multinomial logistic regression
School
Keck School of Medicine
Degree
Master of Science
Degree Program
Biostatistics
Publication Date
04/19/2021
Defense Date
04/19/2021
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
cancer survivors,multinomial logistic regression,OAI-PMH Harvest,unmet needs and services
Language
English
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Electronically uploaded by the author
(provenance)
Advisor
Li, Ming (
committee chair
), Millstein, Josh (
committee member
), Marjoram, Paul (
committee member
)
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shiyus@usc.edu,shiyushen7@gmail.com
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https://doi.org/10.25549/usctheses-c89-448671
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UC11668699
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etd-ShenShiYuS-9501.pdf (filename),usctheses-c89-448671 (legacy record id)
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etd-ShenShiYuS-9501.pdf
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448671
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Thesis
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Shen, ShiYu (Shiyu)
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texts
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University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
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The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
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Tags
cancer survivors
multinomial logistic regression
unmet needs and services