In a randomized trial, if baseline variables are correlated with the outcome, then appropriately adjusting for these can improve precision for estimating the average treatment effect. An example is the analysis of covariance (ANCOVA) estimator, which can be applied 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 has been shown to have the following desirable properties: it is guaranteed to be at least as precise as the standard unadjusted estimator, asymptotically, under no parametric model assumptions; furthermore, it is locally, semiparametric efficient. Recently, estimators have been developed that extend these desirable properties to a more general setting that allows: any real-valued outcome (e.g., binary, count, or continuous), contrasts other than the difference in mean outcomes (such as the relative risk or odds ratio), and estimators based on a large class of generalized linear models (including logistic regression) that may include interaction terms. Though the asymptotic properties of these new estimators have been established, they have not yet been applied to data distributions from randomized trials. We evaluate the practical performance of these estimators using simulations based on resampling data from completed randomized trials in HIV and stroke. In some cases, these estimators substantially improve power compared to standard estimators that ignore baseline variables. Given the large potential gains and relatively small costs, these estimators have potential to be useful in analyzing randomized trials. We provide guidance on how to select among many possible estimators, and recommend an estimator that is a practical compromise between computational complexity and statistical efficiency. R and SAS code is provided, which allows clinical investigators to assess whether these estimators could be useful in their specific trial contexts.
Biostatistics | Statistical Methodology | Statistical Theory
Colantuoni, Elizabeth and Rosenblum, Michael, "LEVERAGING PROGNOSTIC BASELINE VARIABLES TO GAIN PRECISION IN RANDOMIZED TRIALS" (May 2014). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 263.