  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. This is called covariate adjustment. Despite
recommendations by regulatory agencies 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
boundaries in group sequential designs (GSDs). A second obstacle is the
uncertainty at the design stage about how much precision gain will result from
covariate adjustment. We propose a method that modifies the original estimator
so that it becomes compatible with GSDs, while increasing or leaving unchanged
the estimator's precision. Our approach allows the use of any asymptotically
linear 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 a
design adapts to the amount of precision gain and can lead to faster, more
efficient trials, without sacrificing validity or power. We evaluate estimator
performance in simulations that mimic features of a completed stroke trial.
