Inverse probability weighted Bayesian dynamic borrowing for estimation of marginal treatment effects with application to hybrid control arm oncology studies.

We propose an approach for constructing and evaluating the performance of inverse probability weighted robust mixture priors (IPW-RMP) which are applied to the parameters in treatment group-specific marginal models. Our framework allows practitioners to systematically study the robustness of Bayesian dynamic borrowing using the IPW-RMP to enhance the efficiency of inferences on marginal treatment effects (e.g. marginal risk difference) in a target study being planned. A key assumption motivating our work is that the data generation processes for the target study and external data source (e.g. historical study) will not be the same, likely having different distributions for key prognostic factors and possibly different outcome distributions even for individuals who have identical prognostic factors (e.g. different outcome model parameters). We demonstrate the approach using simulation studies based on both binary and time-to-event outcomes, and via a case study based on actual clinical trial data for a solid tumor cancer program. Our simulation results show that when the distribution of risk factors does in fact differ, the IPW-RMP provides improved performance compared to a standard RMP (e.g. increased power and reduced bias of the posterior mean point estimator) with essentially no loss of performance when the risk factor distributions do not differ. Thus, the IPW-RMP can safely be used in any situation where a standard RMP is appropriate.