Monitoring Adverse Events Through Bayesian Nonparametric Clustering Across Studies
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914029134413824 |
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| author | Yuan, Shijie Roberts, Kevin Chandra, Noirrit Kiran Ji, Yuan Müller, Peter |
| author_facet | Yuan, Shijie Roberts, Kevin Chandra, Noirrit Kiran Ji, Yuan Müller, Peter |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_07267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Monitoring Adverse Events Through Bayesian Nonparametric Clustering Across Studies Yuan, Shijie Roberts, Kevin Chandra, Noirrit Kiran Ji, Yuan Müller, Peter Methodology Applications 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. |
| title | Monitoring Adverse Events Through Bayesian Nonparametric Clustering Across Studies |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2509.07267 |