Improving Efficiency and Robustness of Doubly Robust Estimators in the Presence of Coarsened Data

Show full item record

Title: Improving Efficiency and Robustness of Doubly Robust Estimators in the Presence of Coarsened Data
Author: Cao, Weihua
Advisors: Marie Davidian, Committee Chair
Anastasios A. Tsiatis, Committee Co-Chair
Daowen Zhang, Committee Member
Dennis Boos, Committee Member
Abstract: Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The ``usual" doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. In part one of this dissertation, we propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero. The second part of this dissertation focuses on drawing inference on parameters in general models in the presence of monotonely coarsened data, which can be viewed as a generalization of longitudinal data with a monotone missingness pattern, as is the case when subjects drop out of a study. Estimators for parameters of interest include both inverse probability weighted estimators and doubly robust estimators. As a generalization of methods in part one, we propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods. We apply the proposed method to data from an AIDS clinical trial.
Date: 2009-11-03
Degree: PhD
Discipline: Statistics
URI: http://www.lib.ncsu.edu/resolver/1840.16/3774


Files in this item

Files Size Format View
etd.pdf 428.9Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record