Improved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with dropout.

TitleImproved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with dropout.
Publication TypeJournal Article
Year of Publication2011
AuthorsTsiatis, Anastasios A., Marie Davidian, and Weihua Cao
JournalBiometrics
Volume67
Issue2
Pagination536-45
Date Published2011 Jun
ISSN1541-0420
KeywordsAcquired Immunodeficiency Syndrome, Biometry, Computer Simulation, Humans, Longitudinal Studies, Models, Statistical, Statistical Distributions
Abstract

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.

DOI10.1111/j.1541-0420.2010.01476.x
Alternate JournalBiometrics
Original PublicationImproved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with dropout.
PubMed ID20731640
PubMed Central IDPMC3061242
Grant ListP01 CA142538-01 / CA / NCI NIH HHS / United States
R01 CA085848-10 / CA / NCI NIH HHS / United States
R37 AI031789-20 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 CA051962 / CA / NCI NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States
R01 CA051962-19 / CA / NCI NIH HHS / United States
Project: