A Small Sample Correction for Estimating Attributable Risk in Case-Control Studies

TL;DR

This paper introduces a simple correction method for estimating attributable risk in case-control studies, especially useful with small samples or detailed stratification, improving accuracy and reducing noise.

Contribution

It presents a novel correction technique for attributable risk estimation in case-control studies that enhances bias reduction and precision in small samples.

Findings

Correction reduces bias in small samples

Estimates become less noisy with the correction

Method is useful for detailed stratification

Abstract

The attributable risk, often called the population attributable risk, is in many epidemiological contexts a more relevant measure of exposure-disease association than the excess risk, relative risk, or odds ratio. When estimating attributable risk with case-control data and a rare disease, we present a simple correction to the standard approach making it essentially unbiased, and also less noisy. As with analogous corrections given in Jewell (1986) for other measures of association, the adjustment often won't make a substantial difference unless the sample size is very small or point estimates are desired within fine strata, but we discuss the possible utility for applications.