Dose-Response Meta-analysis: Application and Practice using R software

SHIM, SUNG RYUL; LEE, JONGHOO

doi:2019.0.0.e2019006

Methods

Epidemiology and Health 2019;e2019006.
DOI: https://doi.org/10.4178/epih.e2019006 [Accepted] Published online March 28, 2019.

Dose-Response Meta-analysis: Application and Practice using R software

SUNG RYUL SHIM^1,2

, JONGHOO LEE³

¹Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea
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

Correspondence SUNG RYUL SHIM ,Tel: 01035843336, Email: sungryul.shim@gmail.com

Received: March 14, 2019 Accepted after revision: March 28, 2019

Abstract

The objective of this study was to describe general approaches of dose-response meta-analysis that are available for quantitative synthesis of data using R software. We conducted a dose-response meta-analysis using two types of data that included difference in means in continuous data and odds ratio in binary data. The package commands of R software were doseresmeta for overall effect sizes that were separated linear model, quadratic model, and restricted cubic split model in order to well understanding. The effect size according to dose and test for linearity were demonstrated and interpreted through analyzing one- and two-stage DRMA. Especially authors stressed several flexible modellings of exposure for pooling of study-specific trends and graphical presentation of the dose-response trend. This study focused on the practical methods of dose-response meta-analysis rather than theoretical concepts for Korean researchers who were non-majored in statistics. Through this study, authors hope that many Korean researchers will use R software to perform a dose-response meta-analysis more easily and that related research will be activated.

Keywords: Meta-analysis; dose-response; Quadratic model; Restricted cubic spline; Publication bias; R software