Propensity-score-based meta-analytic predictive prior for incorporating real-world and historical data

Abstract

As the availability of real-world data sources (eg, EHRs, claims data, registries) and historical data has rapidly surged in recent years, there is an increasing interest and need from investigators and health authorities to leverage all available information to reduce patient burden and accelerate both drug development and regulatory decision making. Bayesian meta-analytic approaches are a popular historical borrowing method that has been developed to leverage such data using robust hierarchical models. The model structure accounts for various degrees of between-trial heterogeneity, resulting in adaptively discounting the external information in the case of data conflict. In this article, we propose to integrate the propensity score method and Bayesian meta-analytic-predictive (MAP) prior to leverage external real-world and historical data. The propensity score methodology is applied to select a subset of patients from external data that are similar to those in the current study with regards to key baseline covariates and to stratify the selected patients together with those in the current study into more homogeneous strata. The MAP prior approach is used to obtain stratum-specific MAP prior and derive the overall propensity score integrated meta-analytic predictive (PS-MAP) prior. Additionally, we allow for tuning the prior effective sample size for the proposed PS-MAP prior, which quantifies the amount of information borrowed from external data. We evaluate the performance of the proposed PS-MAP prior by comparing it to the existing propensity score-integrated power prior approach in a simulation study and illustrate its implementation with an example of a single-arm phase II trial.

Keywords: Bayesian borrowing; effective sample size; meta-analytic-predictive prior; propensity score; real-world data.