ArticleCPT: pharmacometrics & systems pharmacology2024
Automated Poisson regression exposure-response analysis for binary outcomes with PoissonERM.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- A Perspective on the Use of Poisson Versus Logistic Regression in Exposure-Response Analysis: Insights and Considerations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Automated Poisson regression exposure-response analysis for binary outcomes with PoissonERM.CPT: pharmacometrics & systems pharmacology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PoissonERM is an R package used to conduct exposure-response (ER) analysis on binary outcomes for establishing the relationship between exposure and the occurrence of adverse events (AE). While Poisson regression could be implemented with glm(), PoissonERM provides a simple way to semi-automate the entire analysis and generate an abbreviated report as an R markdown (Rmd) file that includes the essential analysis details with brief conclusions. PoissonERM processes the provided data set using the information from the user's control script and generates summary tables/figures for the exposure metrics, covariates, and event counts of each endpoint (each type of AE). After checking the incidence rate of each AE, the correlation, and missing values in each covariate, an exposure-response model is developed for each endpoint based on the provided specifications. PoissonERM has the flexibility to incorporate and compare multiple scale transformations in its modeling. The best exposure metric is selected based on a univariate model's p-value or deviance (
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