Double robust and efficient estimation of a prognostic model for events in the presence of dependent censoring.

ABSTRACT: In longitudinal data arising from observational or experimental studies, dependent subject drop-out is a common occurrence. If the goal is estimation of the parameters of a marginal complete-data model for the outcome, biased inference will result from fitting the model of interest with only uncensored subjects. For example, investigators are interested in estimating a prognostic model for clinical events in HIV-positive patients, under the counterfactual scenario in which everyone remained on ART (when in reality, only a subset had). Inverse probability of censoring weighting (IPCW) is a popular method that relies on correct estimation of the probability of censoring to produce consistent estimation, but is an inefficient estimator in its standard form. We introduce sequentially augmented

SUBMITTER: Schnitzer ME 

PROVIDER: S-EPMC4679073 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature