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In clinical trials, there is potential to improve precision and reduce therequired sample size by appropriately adjusting for baseline variables in thestatistical analysis. This is called covariate adjustment. Despiterecommendations by regulatory agencies in favor of covariate adjustment, itremains underutilized leading to inefficient trials. We address two obstaclesthat make it challenging to use covariate adjustment. A first obstacle is theincompatibility of many covariate adjusted estimators with commonly usedboundaries in group sequential designs (GSDs). A second obstacle is theuncertainty at the design stage about how much precision gain will result fromcovariate adjustment. We propose a method that modifies the original estimatorso that it becomes compatible with GSDs, while increasing or leaving unchangedthe estimator's precision. Our approach allows the use of any asymptoticallylinear estimator, which covers many estimators used in randomized trials.Building on this, we propose using an information adaptive design, that is,continuing the trial until the required information level is achieved. Such adesign adapts to the amount of precision gain and can lead to faster, moreefficient trials, without sacrificing validity or power. We evaluate estimatorperformance in simulations that mimic features of a completed stroke trial.

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