Alisa J. Stephens, Harvard UniversityJanuary 18, 2012 @ 3:30 - 4:30 pm
Location: Blockley Hall - Room 701
Augmented Estimators for Maximizing Efficiency in the Analysis of Randomized Trials with Correlated Outcomes
Recent methodological advances in covariate adjustment in randomized trials have used semiparametric theory to improve the efficiency of inferences by incorporating baseline covariates. Current literature demonstrates locally efficient estimators of marginal treatment effects when outcomes are independent. We adapt one of these approaches, augmentation of standard estimators, for use within randomized trials with correlated outcomes, such as cluster randomized trials or longitudinal studies. Furthermore, we derive and implement semiparametric locally efficient estimators of marginal mean treatment effects for correlated continuous or binary data. The resulting estimators modify existing generalized estimating equations (GEE) by identifying the efficient score under a mean model for marginal effects when data contain baseline covariates. We demonstrate the potential for imbalance correction and efficiency improvement through application to AIDS Clinical Trial Group Study 398, a longitudinal randomized trial comparing the effects of various protease inhibitors in HIV-positive subjects with antiretroviral therapy failure. The new estimators are compared to various existing estimators through simulation and data analysis.