Monitoring adverse events through Bayesian nonparametric clustering across studies.

Authors

Shijie Yuan, Kevin Roberts, Noirrit Kiran Chandra, Yuan Ji, Peter Müller

Published in

Biometrics. Volume 82. Issue 3. Jul 01, 2026.

Abstract

We introduce a Bayesian nonparametric inference approach for aggregate adverse event (AE) monitoring across studies. The proposed model seamlessly integrates external data from historical trials to define a relevant background rate and accommodates varying levels of covariate granularity (ranging from patient-level details to study-level aggregated summary data). Inference is based on a covariate-dependent product partition model (PPMx). A central element of the model is the ability to group experimental units with similar profiles. We introduce a pairwise similarity measure, with which we set up a random partition of experimental units with comparable covariate profiles, thereby improving the precision of AE rate estimation. Importantly, the proposed framework supports real-time safety monitoring under blinding with a seamless transition to unblinded analyses when indicated. Using one case study and simulation studies, we demonstrate the model's ability to detect safety signals and assess risk under diverse trial scenarios.

PMID:
42466840
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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