Improving Efficiency and Robustness of Doubly Robust Estimators in the Presence of Coarsened Data
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2009-11-03
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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.
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Keywords
missing data, coarsened data
Citation
Degree
PhD
Discipline
Statistics
