Combining Covariate Adjustment with Group Sequential and Information Adaptive Designs to Improve Randomized Trial Efficiency

• Activity Number: 135 - The Use of Baseline Covariates and Early Read-Outs in Adaptive and Group Sequential Designs: Worth the Effort?
• Type: Topic Contributed
• Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Kelly Van Lancker and Joshua Betz and Michael Rosenblum*
• Companies: Johns Hopkins University, Bloomberg School of Public Health, US and Ghent University, Belgium and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
• Keywords: Group sequential designs; Covariate adjustment; randomized trial

Abstract

In clinical trials, there is potential to improve precision and reduce the required sample size by appropriately adjusting for baseline variables in the statistical analysis (i.e., covariate adjustment). Despite recommendations by the FDA and the EMA in favor of covariate adjustment, it remains underutilized leading to inefficient trials. We address two obstacles that make it challenging to use covariate adjustment. A first obstacle is the incompatibility of many covariate adjusted estimators with commonly used stopping boundaries in group sequential designs (GSDs). A second obstacle is the uncertainty at the design stage about how much precision gain will result from it.To address these obstacles, we propose a new method that modifies the original estimator so that it becomes compatible with GSDs, while increasing or leaving unchanged the estimator's precision. Building on this, we propose using an information adaptive design, that is, continuing the trial until the required information level is achieved. Such a design adapts to the amount of precision gain due to covariate adjustment, resulting in trials that are correctly powered and fully leverage prognostic baseline variables.