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Automated Poisson regression exposure-response analysis for binary outcomes with PoissonERM.

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This summary is machine-generated.

PoissonERM is a new R package that simplifies exposure-response (ER) analysis for binary outcomes, automating reporting for adverse event (AE) risk assessment. It aids in understanding dose-response relationships and predicting event rates.

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Area of Science:

  • Pharmacometrics
  • Biostatistics
  • Computational Biology

Background:

  • Exposure-response (ER) analysis is crucial for understanding the relationship between exposure to a substance and the occurrence of adverse events (AEs).
  • Traditional methods using generalized linear models (GLM) can be complex and time-consuming for comprehensive ER analysis.
  • A need exists for streamlined tools that automate analysis and reporting for binary outcomes in ER studies.

Purpose of the Study:

  • To introduce PoissonERM, an R package designed to semi-automate ER analysis for binary outcomes.
  • To facilitate the establishment of relationships between exposure metrics and the incidence of AEs.
  • To provide a user-friendly tool for generating comprehensive analysis reports, including predictions.

Main Methods:

  • PoissonERM utilizes Poisson regression for ER analysis on binary outcomes.
  • The package semi-automates the process, including data processing, model development, and report generation using R markdown.
  • It incorporates flexible modeling options, including multiple scale transformations and backward elimination for covariate selection, while handling correlated covariates.

Main Results:

  • PoissonERM generates summary tables and figures for exposure metrics, covariates, and AE counts.
  • The package selects the optimal exposure metric based on specified criteria (p-value or deviance).
  • It can predict event incidence rates using external data, aiding in the assessment of various dose regimens.

Conclusions:

  • PoissonERM offers a simplified and efficient approach to conducting and reporting ER analyses for binary outcomes.
  • The package enhances the understanding of AE occurrence in relation to exposure levels.
  • Its predictive capabilities support informed decision-making regarding exposure and dosing strategies.