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arXiv Statistics ML By Jungang Zou, Joseph E. Schwartz, Roderick J. A. Little, Nathalie Moise, Qixuan Chen

Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations

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The paper introduces Bayesian Propensity Score Predictive Inference (PSPI), an interaction‑based outcome modeling framework that uses Bayesian additive regression trees and natural cubic splines of the sampling propensity score to extend randomized trial results to broader target populations. PSPI includes variants that apply splines to the treatment effect component alone or to both prognostic and treatment effect components, enabling separate regularization of selection‑related variation and explicit extrapolation beyond boundary knots. Simulation studies demonstrate that spline‑based PSPI models outperform competing methods, achieving lower root mean squared errors and near‑nominal coverage, especially under substantial covariate shift, and in the CODIACS Vanguard trial application PSPI estimates larger intervention benefits than the unadjusted trial estimate.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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