Improving Power in Group Sequential, Randomized Trials by Adjusting for Prognostic Baseline Variables and Short-Term Outcomes

• Activity Number: 123 - Topics for the Statistician Clinical Trialist
• Type: Contributed
• Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Tianchen Qian* and Michael Rosenblum and Huitong Qiu
• Companies: Johns Hopkins University and Johns Hopkins University and Vatic Lab
• Keywords: baseline variable ; group sequential design ; relative efficiency ; semiparametric estimator ; short-term outcome ; treatment effect heterogeneity

Abstract

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 baseline variable, which only explains treatment effect heterogeneity but is marginally uncorrelated with the outcome, can lead to no precision gain. The theory is supported by simulation studies based on data from a trial of a new surgical intervention for treating stroke.