Epidemiol Health.  2019;41:e2019006. 10.4178/epih.e2019006.

Dose-response meta-analysis: application and practice using the R software

  • 1Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea. sungryul.shim@gmail.com
  • 2Urological Biomedicine Research Institute, Soonchunhyang University Hospital, Seoul, Korea.
  • 3Department of Internal Medicine, Jeju National University Hospital, Jeju National University School of Medicine, Jeju, Korea.


The objective of this study was to describe the general approaches of dose-response meta-analysis (DRMA) available for the quantitative synthesis of data using the R software. We conducted a DRMA using two types of data, the difference of means in continuous data and the odds ratio in binary data. The package commands of the R software were "doseresmeta" for the overall effect sizes that were separated into a linear model, quadratic model, and restricted cubic split model for better understanding. The effect sizes according to the dose and a test for linearity were demonstrated and interpreted by analyzing one-stage and two-stage DRMA. The authors examined several flexible models of exposure to pool study-specific trends and made a graphical presentation of the dose-response trend. This study focused on practical methods of DRMA rather than theoretical concepts for researchers who did not major in statistics. The authors hope that this study will help many researchers use the R software to perform DRMAs more easily, and that related research will be pursued.


Meta-analysis; Dose response; Quadratic model; Restricted cubic spline; Nonlinearity; Dosresmeta

MeSH Terms

Linear Models
Odds Ratio
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