title: 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., binary or count), contrasts other than the difference in mean outcomes (such as the relative risk), and estimators based on a large class of generalized linear models (including logistic regression). To the best of our knowledge, we give the first simulation study in the context of randomized trials that compares these estimators. Furthermore, our simulations are not based on parametric models; instead, our simulations are based on resampling data from completed randomized trials in stroke and HIV in order to assess estimator performance in realistic scenarios. We provide practical guidance on when these estimators are likely to provide substantial precision gains and describe a quick assessment method that allows clinical investigators to determine whether these estimators could be useful in their specific trial contexts.
authors: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, U.S.A.
authors: Elizabeth
authors: E
authors: Colantuoni
authors: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, U.S.A.
authors: Michael
authors: M
authors: Rosenblum
mesh_terms: D000704:Analysis of Variance; D044966:Anti-Retroviral Agents / Q000627:therapeutic use; D003198:Computer Simulation; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D020521:Stroke / Q000601:surgery; D016896:Treatment Outcome
chemical_list: D044966:Anti-Retroviral Agents
keywords: prognostic variables; randomized trial; relative efficiency