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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

Open Access
|Nov 2025

Figures & Tables

Figure 1

The directed acyclic graph for the model.

Table 1

Model Fitting functions.

FUNCTIONDESCRIPTION
bhpm.pmBayesian hierarchical model with point-mass mixture for change in log odds of outcome rate.
bhpm.npmBayesian hierarchical model with for change in log odds of outcome rate (no mixture prior).
Table 2

Data format.

COLUMNDESCRIPTION
ClusterPatient clusters
Trt.GrpTreatment identifier (integer)
OutcomeThe clinical outcome of interest
Outcome.GrpA group of related outcomes.
ExposureTotal time under treatment of all patients in Cluster
CountsNumber of Outcomes which have occurred under treatment Exposure
Figure 2

Plots for the mu.theta parameters.

DOI: https://doi.org/10.5334/jors.356 | Journal eISSN: 2049-9647
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

© 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.