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Related Experiment Video

Updated: Jun 7, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Calculating risk and prevalence ratios and differences in R: Developing intuition with a hands-on tutorial and code.

Rachel R Yorlets1, Youjin Lee2, Jason R Gantenberg3

  • 1Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA; Population Studies and Training Center, Brown University, Providence, RI, USA.

Annals of Epidemiology
|November 12, 2024
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Summary

This tutorial guides researchers in choosing appropriate statistical measures for binary outcomes in epidemiology. It provides R code to calculate risk or prevalence ratios, avoiding misleading odds ratios for better research conclusions.

Keywords:
Log-binomialLogistic regressionModified PoissonR programmingRisk differenceRisk ratio

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Context:

  • Epidemiologic research frequently involves binary outcomes, necessitating appropriate statistical measures for association.
  • Current curricula and literature often lack clear guidance on selecting and calculating these measures, potentially leading to misinterpretation.
  • Odds ratios are commonly reported but may not always be the most suitable measure for binary outcomes.

Purpose:

  • To provide a practical tutorial on estimating risk or prevalence ratios (or differences) for binary outcomes.
  • To offer guidance on selecting the appropriate statistical method based on research context.
  • To compare different methods and their results, enhancing understanding and application.

Summary:

  • This study presents a hands-on tutorial using R code to apply, compare, and understand four methods for estimating risk or prevalence ratios.
  • It contrasts these methods with the odds ratio, offering guidance on their appropriate use, strengths, and limitations.
  • The tutorial aims to equip trainees and researchers with the skills to implement and interpret these measures in their own epidemiologic studies.

Impact:

  • Empowers public health researchers and trainees to select and implement appropriate statistical measures for binary outcomes.
  • Reduces the risk of misleading conclusions caused by the use of inappropriate association measures.
  • Enhances the rigor and interpretability of epidemiologic research by promoting the use of risk and prevalence ratios.