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Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes with Clustering

bhpm was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (https://gtr.ukri.org/).

The goal of bhpm is to investigate associations between multiple outcomes and corresponding patient treatments. bhpm implements Bayesian hierarchical models, which allow a stratification of the population into clusters with similar characteristics, and which take advantage of known relationships between clinical outcomes, to determine which outcomes are associated with treatments.

Installation

You can install the released version of bhpm from CRAN with:

install.packages("bhpm")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("rcarragh/bhpm")

Example

This is a basic example which shows how to fit the model:

library(bhpm)
data(demo.cluster.data)
mod.fit <- bhpm.pm(demo.cluster.data, burnin = 100, iter = 200)
#> Memory Model: HIGH
#> MCMC fitting complete.