Leveraging prognostic baseline variables to gain precision in randomized trials.

ABSTRACT: We focus on estimating the average treatment effect in a randomized trial. If baseline variables are correlated with the outcome, then appropriately adjusting for these variables can improve precision. An example is the analysis of covariance (ANCOVA) estimator, which applies when the outcome is continuous, the quantity of interest is the difference in mean outcomes comparing treatment versus control, and a linear model with only main effects is used. ANCOVA is guaranteed to be at least as precise as the standard unadjusted estimator, asymptotically, under no parametric model assumptions and also is locally semiparametric efficient. Recently, several estimators have been developed that extend these desirable properties to more general settings that allow any real-valued outcome (e.g., bina

SUBMITTER: Colantuoni E 

PROVIDER: S-EPMC5018399 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature