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

TitleDouble robust and efficient estimation of a prognostic model for events in the presence of dependent censoring.
Publication TypeJournal Article
Year of Publication2016
AuthorsSchnitzer ME, Lok JJ, Bosch RJ
JournalBiostatistics
Volume17
Issue1
Pagination165-77
Date Published2016 Jan
ISSN1468-4357
KeywordsAnti-Retroviral Agents, Data Interpretation, Statistical, HIV Infections, Humans, Likelihood Functions, Models, Statistical
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 regression (SAR), an adaptation of the Bang and Robins (2005. Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962-972.) method to estimate a complete-data prediction model, adjusting for longitudinal missing at random censoring. In addition, we propose a closely related non-parametric approach using targeted maximum likelihood estimation (TMLE; van der Laan and Rubin, 2006. Targeted maximum likelihood learning. The International Journal of Biostatistics 2 (1), Article 11). We compare IPCW, SAR, and TMLE (implemented parametrically and with Super Learner) through simulation and the above-mentioned case study.

DOI10.1093/biostatistics/kxv028
Alternate JournalBiostatistics
PubMed ID26224070
PubMed Central IDPMC4679073
Grant ListAI 38855 / AI / NIAID NIH HHS / United States
UM1 AI068634 / AI / NIAID NIH HHS / United States
R01AI100762 / AI / NIAID NIH HHS / United States
AI 68634 / AI / NIAID NIH HHS / United States
AI 68636 / AI / NIAID NIH HHS / United States
AI 38858 / AI / NIAID NIH HHS / United States
UM1 AI068636 / AI / NIAID NIH HHS / United States