Bayesian prognostic covariate adjustment

TL;DR

This paper introduces a Bayesian method that combines prognostic covariate adjustment with empirical priors from past trial data to improve statistical power in clinical trials while controlling type I error.

Contribution

It develops a Bayesian framework that integrates prognostic scores with learned priors, enhancing efficiency and power in treatment effect estimation.

Findings

The method increases statistical power compared to traditional covariate adjustment.

It maintains control over type I error under reasonable conditions.

Demonstrated effectiveness in simulations and Alzheimer's trial analysis.

Abstract

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.