Relative Risk Regression

The most common way to model associations with a dichotomous outcome variable is through logistic regression. Such associations can instead be estimated and communicated as relative risks (sometimes called risk ratios or prevalence ratios) under certain circumstances. In the case of a rare outcome (i.e. the prevalence of the outcome is ≤ 10%) the odds ratio will approximate the risk ratio and relative risk regression is not necessary. However, if the outcome is not rare and the study participants are sampled in such a way that the baseline risk in the unexposed can be estimated (e.g. cohort study, cross-sectional study or case-cohort study) then relative risk regression can be performed and may in fact be the preferred analysis method. These recommendations assume that the association itself is the quantity of interest (rather than optimal prediction) and that a dichotomous outcome is meaningful. Some analyses ignore important information by treating a continuous quantity as dichotomous or by discarding time to event data in order to use logistic regression, and relative risk regression does not fix this problem. Relative risk regression can be implemented quite easily with most standard software packages.

RESOURCE LIST

EDUCATIONAL WEBSITES

METHODOLOGICAL ARTICLES

What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes

Estimating the relative risk in cohort studies and clinical trials of common outcomes

A conceptual and empirical examination of justifications for dichotomization

Clinically useful measures of effect in binary analyses of randomized trials

At Odds: Concerns Raised by Using Odds Ratios for Continuous or Common Dichotomous Outcomes

Distributional interaction: Interpretational problems when using incidence odds ratios to assess interaction

SOFTWARE/PROGRAMMING ARTICLES

Easy SAS calculations for risk or prevalence ratios and differences

Alternatives for logistic regression in cross-sectional studies

Relative risk regression: reliable and flexible methods for log-binomial models

EXAMPLE APPLICATION ARTICLES

Application of different statistical methods to estimate relative risk for self-reported health complaints

Long-term harm of low preparedness for a wife’s death from cancer–a population-based study

The Changing Distribution and Determinants of Obesity in the Neighborhoods of New York City, 2003-2007