Bayesian prognostic covariate adjustment

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.

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.
Press

Unlearn.AI named to the 2021 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups

Blog

Welcoming Dr. Taylor to Unlearn.AI’s Board of Directors

Press

Unlearn Appoints AstraZeneca’s Chief Medical Officer Ann E. Taylor, M.D. to Board of Directors

Ann E. Taylor, M.D., Chief Medical Officer at AstraZeneca, has joined the Unlearn Board of Directors.
I’m honored to have such an inspirational and experienced leader like Dr. Taylor join our board.
The AI 100 is CB Insights' annual list of the 100 most promising private AI companies in the world.