Automated Poisson regression exposure–response analysis for binary outcomes with PoissonERM

Abstract

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 ( Δ D ) as specified. If a covariate search is specified in the control script, the final model is developed using backward elimination. PoissonERM identifies and avoids highly correlated covariates in the final model development of each endpoint. Predicting event incidence rates using external (simulated) exposure metric data is an additional functionality in PoissonERM, which is useful to understand the event occurrence associated with certain dose regimens. The summary outputs of the cleaned data, model developments, and predictions are saved in the working folder and can be compiled into a HTML report using Rmd.