Risk Ratio regression -- simple concept yet complex computation

TL;DR

This paper discusses the challenges and recent computational solutions for estimating the risk ratio in epidemiology, emphasizing its interpretability and the complexity of its computation compared to odds ratios.

Contribution

It highlights the longstanding issues in estimating risk ratios and reviews recent computational methods that improve convergence and robustness.

Findings

Recent methods improve convergence in risk ratio estimation

Doubly robust estimates enhance reliability

Risk ratio interpretation remains straightforward

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 remains 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 that help overcome the issue of convergence and provide doubly robust estimates of RR.