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Meta-Analytic Methods for Public Health Research

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Innovative Statistical Methods for Public Health Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

This chapter presents an overview on meta-analysis (MA) intended for public health researchers to understand and to apply the methods of MA. Emphasis is focused on classical statistical methods for estimation of the parameters of interest in MA as well as recent development in research in MA. Specifically, univariate and multivariate fixed- and random-effects MAs, as well as meta-regression are discussed. All methods are illustrated by examples of published MA in public health research. We demonstrate how these approaches can be implemented using software packages in R.

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Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, The George Washington University, Washington, DC, USA

    Yan Ma & Wei Zhang

  2. School of Social Work, University of North Carolina, Chapel Hill, NC, 27599, USA

    Ding-Geng Chen

Authors
  1. Yan Ma
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  2. Wei Zhang
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  3. Ding-Geng Chen
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Corresponding author

Correspondence to Yan Ma .

Editor information

Editors and Affiliations

  1. Wallace H. Kuralt Distinguished Professor, Director of Statistical Development and Consultation, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Arizona State University, Tempe, Arizona, USA

    Jeffrey Wilson

Appendix

Appendix

We performed all our analyses in R using the mvmeta package. In this appendix we provide the R code written for the meta-analysis and meta-regression in Example 1.

###R code for fixed effects UMA:

$$\displaystyle{\text{mvmeta(PD,S=var.PD,data=ex.data,method=\textquotedblleft fixed\textquotedblright )}}$$

where var.PD stands for the within-study variance of PD.

###R code for random effects UMA:

$$\displaystyle{\text{mvmeta(PD,S=var.PD,data=ex.data,method=\textquotedblleft REML\textquotedblright )}}$$

###R code for fixed effects BMA:

$$\displaystyle{\text{mvmeta(cbind(PD,AL),S=cbind(var.PD,cov.PD.AL, var.AL),data=ex.data,method=\textquotedblleft fixed\textquotedblright )}}$$

where cov.PD.AL stands for the within-study covariance of PD and AL.

###R code for random effects BMA:

$$\displaystyle{\text{mvmeta(cbind(PD,AL), S=cbind(var.PD,cov.PD.AL, var.AL), data=ex.data,method=\textquotedblleft REML\textquotedblright )}}$$

###R code for fixed effects univariate MR:

$$\displaystyle{\mbox{mvmeta(PD$\tilde{}$ year,S=var.PD, data=ex.data,method=\textquotedblleft fixed\textquotedblright )}}$$

###R code for random effects univariate MR:

$$\displaystyle{\mbox{mvmeta(PD$\tilde{}$ year,S=var.PD, data=ex.data,method=\textquotedblleft REML\textquotedblright )}}$$

###R code for fixed effects bivariate MR:

$$\displaystyle{\text{mvmeta(cbind(PD,AL)$\tilde{}$ year,S=cbind(var.PD,cov.PD.AL, var.AL), data=ex.data,method=\textquotedblleft fixed\textquotedblright )}}$$

###R code for random effects bivariate MR:

$$\displaystyle{\text{mvmeta(cbind(PD,AL)$\tilde{}$ year,S=cbind(var.PD,cov.PD.AL, var.AL), data=ex.data,method=\textquotedblleft REML\textquotedblright )}}$$

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Ma, Y., Zhang, W., Chen, DG. (2015). Meta-Analytic Methods for Public Health Research. In: Chen, DG., Wilson, J. (eds) Innovative Statistical Methods for Public Health Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18536-1_15

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