Leveraging prognostic baseline variables to gain precision in randomized trials.

TitleLeveraging prognostic baseline variables to gain precision in randomized trials.
Publication TypeJournal Article
Year of Publication2015
AuthorsColantuoni E, Rosenblum M
JournalStat Med
Date Published2015 Aug 15
KeywordsAnalysis of Variance, Anti-Retroviral Agents, Computer Simulation, HIV Infections, Humans, Prognosis, Randomized Controlled Trials as Topic, Research Design, Stroke, Treatment Outcome

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.

Alternate JournalStat Med
PubMed ID25872751
Grant List5R01 NS046309-07 / NS / NINDS NIH HHS / United States
5U01 NS062851-04 / NS / NINDS NIH HHS / United States
R01 NS046309 / NS / NINDS NIH HHS / United States
U01 NS062851 / NS / NINDS NIH HHS / United States