Dynamic borrowing of historical controls adjusting for covariates in vaccine efficacy clinical trials

Abstract

Traditional vaccine efficacy trials usually use fixed designs and often require large sample sizes. Recruiting a large number of subjects can make the trial expensive, long, and difficult to conduct. A possible approach to reduce the sample size and speed up the development is to use historical controls. In this paper, we extend the robust mixture prior (RMP) approach (a well established approach for Bayesian dynamic borrowing of historical controls) to adjust for covariates. The adjustment is done using classical methods from causal inference: inverse probability of treatment weighting, G-computation and double-robust estimation. We evaluate these covariate-adjusted RMP approaches using a comprehensive simulation study and demonstrate their use by performing a retrospective analysis of a prophylactic human papillomavirus vaccine efficacy trial. Adjusting for covariates reduces the drift between current and historical controls, with a beneficial effect on bias, control of type I error and power.

Keywords: Bayesian inference; G‐estimation; adaptive design; causal inference; clinical trials; double robust estimator; dynamic borrowing; historical controls; propensity score; robust mixture prior; vaccine efficacy trial.