Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health

• Type of Material: Article
• Authors: Jessica A Grembi, Stanford University; Elizabeth T Rogawski McQuade, Emory University
• Language: English
• Date: 2022-07-01
• Publisher: PLoS ONE
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Grembi, Rogawski McQuade
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0265368
• Title of Journal or Parent Work: PLoS ONE
• Volume: 17
• Issue: 7 July
• Start Page: e0265368
• End Page: e0265368
• Grant/Funding Information: This work was supported by the National Institutes of Health, National Institute of Allergy and Infectious Diseases (grant K01AI130326 to ETRM; https://www.niaid.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: Odds Ratio; Public Health; Incidence; Causality; Models, Statistical
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Medicine and Surgery

Abstract

Common statistical modeling methods do not necessarily produce the most relevant or interpretable effect estimates to communicate risk. Overreliance on the odds ratio and relative effect measures limit the potential impact of epidemiologic and public health research. We created a straightforward R package, called riskCommunicator, to facilitate the presentation of a variety of effect measures, including risk differences and ratios, number needed to treat, incidence rate differences and ratios, and mean differences. The riskCommunicator package uses g-computation with parametric regression models and bootstrapping for confidence intervals to estimate effect measures in time-fixed data. We demonstrate the utility of the package using data from the Framingham Heart Study to estimate the effect of prevalent diabetes on the 24-year risk of cardiovascular disease or death. The package promotes the communication of public-health relevant effects and is accessible to a broad range of epidemiologists and health researchers with little to no expertise in causal inference methods or advanced coding.