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

Sung Ryul Shim1,2, Jonghoo Lee3

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

Epidemiology and Health
|April 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces practical dose-response meta-analysis (DRMA) methods using R software. It guides researchers in synthesizing continuous and binary data, simplifying complex analyses for broader application.

Keywords:
Dose responseDosresmetaMeta-analysisNonlinearityQuadratic modelRestricted cubic spline

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Area of Science:

  • Biostatistics
  • Quantitative Synthesis
  • Epidemiology

Background:

  • Dose-response meta-analysis (DRMA) is crucial for synthesizing quantitative data.
  • Existing methods can be complex for non-statisticians.
  • Accessible tools are needed to facilitate DRMA implementation.

Purpose of the Study:

  • To describe general approaches for dose-response meta-analysis (DRMA).
  • To demonstrate practical data synthesis using R software.
  • To aid researchers in performing DRMAs without extensive statistical background.

Main Methods:

  • Conducted DRMA for continuous (difference of means) and binary (odds ratio) data.
  • Utilized R software package 'doseresmeta' for effect size calculations.
  • Applied linear, quadratic, and restricted cubic spline models for trend analysis.

Main Results:

  • Demonstrated effect sizes based on exposure dose and tested for linearity.
  • Interpreted results from one-stage and two-stage DRMA models.
  • Presented flexible models to pool study-specific trends with graphical outputs.

Conclusions:

  • The study provides practical guidance on performing DRMA using R.
  • Simplified methods aim to increase the use of DRMA in research.
  • Encourages further research applying these accessible DRMA techniques.