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Fixed Effect and Random Effects Meta-Analysis

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Meta-Analysis with R

Part of the book series: Use R! ((USE R))

Abstract

In this chapter we describe the two main methods of meta-analysis, fixed effect model and random effects model, and how to perform the analysis in R. For both models the inverse variance method is introduced for estimation. The pros and cons of these methods in various contexts have been debated at length in the literature [9, 28, 29, 41], without any conclusive resolution. Here, we briefly describe each model, and how it is estimated in the R package meta [33, 34].

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Notes

  1. 1.

    If you did not already install R package meta do so using R command install. packages("meta").

  2. 2.

    Note we could use a pooled estimate of the sample variance, but this assumes that the response variance is the same in the two groups which will not be true in general.

  3. 3.

    The parentheses are not mandatory to select Boner 1988; we use them only to make the R code more accessible.

  4. 4.

    The parentheses are mandatory to exclude Boner 1988 using the variables author and year.

  5. 5.

    R command: install.packages("metafor") .

  6. 6.

    R code to create the forest plot is given in the web-appendix.

  7. 7.

    We did not do this in the creation of R object mc1.

  8. 8.

    R function update is a generic function like print or summary.

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Authors and Affiliations

  1. Institute for Medical Biometry and Statistics, Medical Center – University of Freiburg, Freiburg, Germany

    Guido Schwarzer & Gerta Rücker

  2. MRC Clinical Trials Unit, London and London School of Hygiene and Tropical Medicine, London, UK

    James R. Carpenter

Authors
  1. Guido Schwarzer
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  2. James R. Carpenter
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  3. Gerta Rücker
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Schwarzer, G., Carpenter, J.R., Rücker, G. (2015). Fixed Effect and Random Effects Meta-Analysis. In: Meta-Analysis with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-21416-0_2

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