The adjustment of random baseline measurements in treatment effect estimation

The analysis of covariance (ANCOVA) is often used in analyzing clinical trials that make use of “baseline” response. Unlike Crager [1987. Analysis of covariance in parallel-group clinical trials with pretreatment baseline. Biometrics 43, 895-901.], we show that for random baseline covariate, the ordinary least squares (OLS)-based ANCOVA method provides invalid unconditional inference for the test of treatment effect when heterogeneous regression exists for the baseline covariate across different treatments. To correctly address the random feature of baseline response, we propose to directly model the pre- and post-treatment measurements as repeated outcome values of a subject. This bivariate modeling method is evaluated and compared with the ANCOVA method by a simulation study under a wide variety of settings. We find that the bivariate modeling method, applying the Kenward-Roger approximation and assuming distinct general variance-covariance matrix for different treatments, performs the best in analyzing a clinical trial that makes use of random baseline measurements.