Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout

• Published in: Biometrics Vol. 67; no. 2; pp. 536 - 545
• Main Authors: Tsiatis, Anastasios A., Davidian, Marie, Cao, Weihua
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.06.2011; Wiley-Blackwell; Blackwell Publishing Ltd
• Subjects: Acquired Immunodeficiency Syndrome; Bioinformatics; BIOMETRIC METHODOLOGY; Biometrics; biometry; Biometry - methods; clinical trials; Coarsening at random; Computer Simulation; Consistent estimators; Discrete hazard; Dropout; Estimating techniques; Estimation methods; Estimation theory; Estimators; Humans; Longitudinal data; Longitudinal Studies; Mathematical models; Missing at random; Models, Statistical; Population mean; Regression analysis; School dropouts; Statistical Distributions; Statistical methods; statistical models; Statistical variance
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2010.01476.x

A routine challenge is that of making inference on parameters in a statistical model of interest from longitudinal data subject to dropout, which are a special case of the more general setting of monotonely coarsened data. Considerable recent attention has focused on doubly robust (DR) 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. DR estimators have been criticized for potentially disastrous performance when both of these models are even only mildly misspecified. We propose a DR estimator applicable in general monotone coarsening problems that achieves comparable or improved performance relative to existing DR methods, which we demonstrate via simulation studies and by application to data from an AIDS clinical trial.