A Practical Guide to Conducting Dose-Response Meta-Analyses in Epidemiology

TL;DR

This paper provides a step-by-step guide for analyzing how varying levels of risk factors, like alcohol consumption, relate to disease outcomes using meta-analysis.

Contribution

The paper introduces a three-step method to address challenges in dose-response meta-analyses, such as inconsistent risk measures and reference categories.

Findings

Harmonizing risk measures is crucial for accurate dose-response meta-analyses.

Homogenizing the reference category improves consistency across studies.

Selecting appropriate meta-regression models helps determine the shape of the dose-response curve.

Abstract

Dose-response relationships between continuous risk factors and disease outcomes are necessary for understanding the risks related to different levels of exposure. Dose-response risk curves can lead to more targeted public health messaging, prevention efforts, and policy implementation. Meta-analyses are often used to combine statistical results from different studies and can be used to model dose-response relationships. However, several challenges are encountered when performing dose-response meta-analysis, such as having heterogeneous reference categories, inconsistent measures of risk, and determining the most accurate shape of the curve. In this paper, we propose a three-step process for estimating dose-response relationships via meta-analysis, which involves: 1) harmonizing the measures of risk, 2) homogenizing the reference category, and 3) selecting meta-regression models. We use data obtained from a systematic review on the dose-response relationship between alcohol consumption and the risk of chronic liver disease to provide an example of the proposed process.

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