Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies

TLDR

Recent advances in biomedical science and information technology have made large‑scale real‑world health data available, and high‑quality real‑world evidence can be transformed into regulatory‑relevant scientific evidence using propensity score methods and Bayesian inference. This study extends the Bayesian power prior approach for single‑arm trials to incorporate external real‑world data. The authors first use propensity scores to select a comparable subset of real‑world patients and stratify them with the trial cohort, then apply a stratum‑specific power prior to obtain posterior distributions that are combined for overall inference. Simulation and a hypothetical regulatory example show that the proposed propensity‑score‑integrated power prior outperforms the ordinary power prior in terms of performance.

Abstract

We are now at an amazing time for medical product development in drugs, biological products and medical devices. As a result of dramatic recent advances in biomedical science, information technology and engineering, ``big data'' from health care in the real-world have become available. Although big data may not necessarily be attuned to provide the preponderance of evidence to a clinical study, high-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision-making using proven analytical methods and techniques, such as propensity score methodology and Bayesian inference. In this paper, we extend the Bayesian power prior approach for a single-arm study (the current study) to leverage external real-world data. We use propensity score methodology to pre-select a subset of real-world data containing patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest. We evaluate the performance of the proposed method as compared to that of the ordinary power prior approach by simulation and illustrate its implementation using a hypothetical example, based on our regulatory review experience.