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A STUDY OF METHODS FOR MISSING DATA PROBLEMS IN
EPIDEMIOLOGIC STUDIES WITH HISTORICAL EXPOSURES
by
Xinbo Zhang
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful¯llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOSTATISTICS)
May 2009
Copyright 2009 Xinbo Zhang
Object Description
| Title | A study of methods for missing data problems in epidemiologic studies with historical exposures |
| Author | Zhang, Xinbo |
| Author email | xinbozha@usc.edu; xinbozhang76@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biostatistics |
| School | Keck School of Medicine |
| Date defended/completed | 2008-12-19 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-05-08 |
| Advisor (committee chair) | Langholz, Bryan |
| Advisor (committee member) |
Cockburn, Myles G. Stram, Daniel O. Berhane, Kiros Goldstein, Larry M. |
| Abstract | In this thesis we consider methods for a specific missing pattern where missing values occur across the exposure history of an individual, thus creating gaps and holes in the exposure history. We propose a missing indicator induced intensity (MIND) method under the rare disease assumption. The idea originated from Prentice (1982) and the theoretical development can find its root within the Cox regression framework under cohort design, in which the essential part is the parametrization of the induced intensity. The parametrization of the "induced'' intensity actually reflects the missing mechanism, therefore the missing mechanism assumption such as missing completely at random (MCAR), missing at random (MAR) found in other literature is no longer required.; The MIND method is compared against simple imputation methods in a Monte Carlo simulation study under logistic model with case-control sampling and demonstrates to be better in term of bias and efficiency compared to the single imputation methods considered, and far superior to the complete case analysis method in term of relative efficiency. The method is shown to reach an asymptotic efficiency equal to the expected non-missing proportion under cohort design, assuming the exposure and the missingness as a pair is independently and identically distributed across different years. Under nested case-control sampling design, the asymptotic efficiency varies slightly but stays close to cohort design nonetheless. Under rare disease assumption, the method can be bridged back to case-control design based on logistic model. The method is then applied to the University of Southern California prostate cancer-pesticide pilot study to assess its performance. The MIND method is overall efficient and flexible in solving the missing data problem where the missingness occurs in the exposure history. The method can be further improved with better parametrization for the induced intensity under complex situations, especially when the exposure is correlation over years. |
| Keyword | asymptotic efficiency; case-control study; Cox regression; exposure history; logistic regression; missing data |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2211 |
| Contributing entity | University of Southern California |
| Rights | Zhang, Xinbo |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@dots.usc.edu |
| Filename | etd-Zhang-2631 |
| Archival file | uscthesesreloadpub_Volume51/etd-Zhang-2631.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@dots.usc.edu |
| Full text | A STUDY OF METHODS FOR MISSING DATA PROBLEMS IN EPIDEMIOLOGIC STUDIES WITH HISTORICAL EXPOSURES by Xinbo Zhang A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful¯llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOSTATISTICS) May 2009 Copyright 2009 Xinbo Zhang |
