IMPROVING POWER IN GROUP SEQUENTIAL, RANDOMIZED TRIALS BY ADJUSTING FOR PROGNOSTIC BASELINE VARIABLES AND SHORT-TERM OUTCOMES

In group sequential designs, adjusting for baseline variables and short-term outcomes can lead to increased power and reduced sample size. We derive formulas for the precision gain from such variable adjustment using semiparametric estimators for the average treatment effect, and give new results on what conditions lead to substantial power gains and sample size reductions. The formulas reveal how the impact of prognostic variables on the precision gain is modified by the number of pipeline participants, analysis timing, enrollment rate, and treatment effect heterogeneity, when the semiparametric estimator uses correctly specified models. Given set prognostic value of baseline variables and short-term outcomes within each arm, the precision gain is maximal when there is no treatment effect heterogeneity. In contrast, a purely predictive ∗Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA; e-mail: tqian2@jhu.edu †Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA; e-mail: michael.a.rosenblum@gmail.com ‡Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA; e-mail: hqiu@jhsph.edu