Bayesian prognostic covariate adjustment
This method offers increased statistical power and controlled type I error for researchers conducting clinical trials, particularly in diseases like Alzheimer's.
This paper proposes a Bayesian framework that combines prognostic covariate adjustment with an empirical prior distribution learned from past trials to increase the efficiency of treatment effect estimates in clinical trials. The method theoretically offers a substantial increase in statistical power while limiting the type I error rate under reasonable conditions.
Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates via covariate adjustment. Here we go further, utilizing a Bayesian framework that combines prognostic covariate adjustment with an empirical prior distribution learned from the predictive performances of the prognostic model on past trials. The Bayesian approach interpolates between prognostic covariate adjustment with strict type I error control when the prior is diffuse, and a single-arm trial when the prior is sharply peaked. This method is shown theoretically to offer a substantial increase in statistical power, while limiting the type I error rate under reasonable conditions. We demonstrate the utility of our method in simulations and with an analysis of a past Alzheimer's disease clinical trial.