Multivariate Bayesian Dynamic Borrowing for Repeated Measures Data With Application to External Control Arms in Open-Label Extension Studies

Abstract

Borrowing analyses are increasingly important in clinical trials. We develop a method for using robust mixture priors in multivariate dynamic borrowing. The method was motivated by a desire to produce causally valid, long-term treatment effect estimates of a continuous endpoint from a single active-arm open-label extension study following a randomized clinical trial by dynamically incorporating prior beliefs from a long-term external control arm. The proposed method is a generally applicable Bayesian dynamic borrowing analysis for estimates of multivariate summary metrics based on a multivariate normal likelihood function for various parameter models, some of which we describe. There are important connections to estimation incorporating a prior belief for a hypothetical estimand strategy, that is, had the event not occurred, for intercurrent events which lead to missing data.