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

Authors

  • Huan Jiang
    Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
  • Jürgen Rehm
    Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
  • Charlotte Probst
    Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
  • Alexander Tran
    Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
  • Shannon Lange
    Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
  • Laura Llamosas-Falcón
    Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada

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.