Improving operating characteristics of clinical trials by augmenting control arm using propensity score-weighted borrowing-by-parts power prior

2026-01-07

AI Summary

This study addresses the challenge of bias arising from covariate distributional shifts or outcome heterogeneity when borrowing external control data in clinical trials. To mitigate this, the authors propose the PSW-BPP framework, which uniquely integrates propensity score weighting with partitioned power priors. The approach aligns population distributions via causal covariate adjustment and introduces separate borrowing parameters for the mean and variance components of the outcome model. A novel minimum plausibility index (mPI) is incorporated to automatically calibrate the strength of borrowing, thereby enhancing robustness against prior-data conflict. Simulation studies and real-data analyses demonstrate that, under moderate covariate imbalance and outcome heterogeneity, PSW-BPP substantially improves estimation efficiency and stability compared to strategies that either forgo borrowing or employ fixed borrowing strengths.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Probabilistic ProgrammingSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Borrowing external data can improve estimation efficiency but may introduce bias when populations differ in covariate distributions or outcome variability. A proper balance needs to be maintained between the two datasets to justify the borrowing. We propose a propensity score weighting borrowing-by-parts power prior (PSW-BPP) that integrates causal covariate adjustment through propensity score weighting with a flexible Bayesian borrowing approach to address these challenges in a unified framework. The proposed approach first applies propensity score weighting to align the covariate distribution of the external data with that of the current study, thereby targeting a common estimand and reducing confounding due to population heterogeneity. The weighted external likelihood is then incorporated into a Bayesian model through a borrowing-by-parts power prior, which allows distinct power parameters for the mean and variance components of the likelihood, enabling differential and calibrated information borrowing. Additionally, we adopt the idea of the minimal plausibility index (mPI) to calculate the power parameters. This separate borrowing provides greater robustness to prior-data conflict compared with traditional power prior methods that impose a single borrowing parameter. We study the operating characteristics of PSW-BPP through extensive simulation and a real data example. Simulation studies demonstrate that PSW-BPP yields more efficient and stable estimation than no borrowing and fixed borrowing, particularly under moderate covariate imbalance and outcome heterogeneity. The proposed framework offers a principled and extensible methodological contribution for Bayesian inference with external data in observational and hybrid study designs.

Problem

• external data borrowing
• population heterogeneity
• covariate imbalance
• bias-variance tradeoff
• clinical trial efficiency

Innovation

• propensity score weighting
• borrowing-by-parts power prior
• Bayesian external borrowing
• minimal plausibility index
• heterogeneous populations