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

Abstract

AbstractCommon 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 absolute 24-year risk of cardiovascular disease or death was 30% (95% confidence interval (CI): 22, 38) higher among subjects with diabetes compared to subjects without diabetes at baseline. The relative 24-year risk was 55% (95% CI: 40, 70) higher. Because the outcome was common (41.8%), the odds ratio (4.55) is highly inflated compared to the risk ratio (1.55). An expected 4 additional persons would need to have diabetes at baseline to observe an increase in the number of cases of cardiovascular disease or death by 1 over 24 years of follow-up.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.