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1.  Improved Doubly Robust Estimation when Data are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout 
Biometrics  2010;67(2):536-545.
Summary
A routine challenge is that of making inference on parameters in a statistical model of interest from longitudinal data subject to drop out, which are a special case of the more general setting of monotonely coarsened data. Considerable recent attention has focused on doubly robust estimators, which in this context involve positing models for both the missingness (more generally, coarsening) mechanism and aspects of the distribution of the full data, that have the appealing property of yielding consistent inferences if only one of these models is correctly specified. Doubly robust estimators have been criticized for potentially disastrous performance when both of these models are even only mildly misspecified. We propose a doubly robust estimator applicable in general monotone coarsening problems that achieves comparable or improved performance relative to existing doubly robust methods, which we demonstrate via simulation studies and by application to data from an AIDS clinical trial.
doi:10.1111/j.1541-0420.2010.01476.x
PMCID: PMC3061242  PMID: 20731640
Coarsening at random; Discrete hazard; Dropout; Longitudinal data; Missing at random
2.  Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data 
Biometrika  2009;96(3):723-734.
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. 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.
doi:10.1093/biomet/asp033
PMCID: PMC2798744  PMID: 20161511
Causal inference; Enhanced propensity score model; Missing at random; No unmeasured confounders; Outcome regression

Results 1-2 (2)