Dynamic borrowing via covariate-adjusted self-adapting mixture priors in clinical trials

Incorporating historical data into clinical trials can enhance statistical power, but it risks introducing bias if historical and current populations differ. Existing Bayesian borrowing methods often apply a single, prespecified weight to historical data, failing to account for patient-level covariate differences. To address this, we propose a covariate-adjusted self-adapting mixture (CASAM) prior, a Bayesian framework that enables covariate-dependent dynamic borrowing from external data. CASAM adaptively determines the degree of information borrowing by assessing the compatibility between historical and current data conditional on covariates, automatically down-weighting historical information when residual heterogeneity is detected. To further address covariate distribution mismatch between historical and current trials, we develop a propensity-score-integrated implementation of CASAM, which first re-weights historical controls to align their covariate distribution with that of the current control arm before applying the CASAM prior. This layered strategy allows CASAM to focus on structural similarity while the propensity-score adjustment corrects for population-level imbalance. Together, CASAM and its propensity-score-based implementation provide an empirical-Bayes framework for adaptive borrowing that combines covariate alignment with outcome-model compatibility assessment.