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Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes and Clustering with Applications to Observational Health Data Cover

Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes and Clustering with Applications to Observational Health Data

Journal of Open Research Software
Volume 13 (2025): Issue 1
By: Raymond Carragher,  Tanja Mueller,  Marion Bennie and  Chris Robertson  
Open Access
|Nov 2025

Authors

Raymond Carragher

raymond.carragher@uws.ac.uk

School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley

Tanja Mueller

tanja.muller@strath.ac.uk

Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow

Marion Bennie

marion.bennie@strath.ac.uk

Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow; Public Health and Intelligence Strategic Business Unit, NHS National Services Scotland, Glasgow

Chris Robertson

chris.robertson@strath.ac.uk

Department of Mathematics and Statistics, University of Strathclyde, Glasgow; Health Protection Scotland, NHS National Services Scotland, Glasgow
DOI: https://doi.org/10.5334/jors.356 | Journal eISSN: 2049-9647
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Language: English
Submitted on: Oct 28, 2020
Accepted on: Nov 14, 2025
Published on: Nov 24, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Bayesian Hierarchy,
Stratified analysis,
Clusters,
Multiple comparisons,
Pharmacovigilance,
Safety outcomes,
Health Data

© 2025 Raymond Carragher, Tanja Mueller, Marion Bennie, Chris Robertson, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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