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

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  • Jessica A Grembi
  • Elizabeth T Rogawski McQuade

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.

Suggested Citation

  • Jessica A Grembi & Elizabeth T Rogawski McQuade, 2022. "Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0265368
    DOI: 10.1371/journal.pone.0265368
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    1. repec:bla:jorssa:v:163:y:2000:i:3:p:381-402 is not listed on IDEAS
    2. repec:plo:pmed00:0040297 is not listed on IDEAS
    3. J. L. Hutton, 2000. "Number needed to treat: properties and problems," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 381-402.
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    Cited by:

    1. Stefan Nygaard Hansen & Morten Overgaard, 2025. "Variance estimation for average treatment effects estimated by g-computation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(4), pages 419-443, May.

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