Reflection on modern methods: risk ratio regression-simple concept yet complex computation

Murthy N Mittinty; John Lynch

doi:10.1093/ije/dyac220

Reflection on modern methods: risk ratio regression-simple concept yet complex computation

Int J Epidemiol. 2023 Feb 8;52(1):309-314. doi: 10.1093/ije/dyac220.

Authors

Murthy N Mittinty^{1

2}, John Lynch^{1

2

3}

Affiliations

¹ School of Public Health, University of Adelaide, Adelaide, SA, Australia.
² Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.
³ Population Health Sciences, University of Bristol, Bristol, UK.

Abstract

The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.

Keywords: Relative risk; epidemiology; generalized linear models; regression.

MeSH terms

Humans
Logistic Models
Odds Ratio*