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Meta-Analysis Using R Statistical Software

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Modern Biostatistical Methods for Evidence-Based Global Health Research

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

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Abstract

A systematic review and meta-analysis (SRMA) of results from randomized clinical trials (RCTs) is considered the highest level of evidence in determining comparative effect of health intervention for a given disease or condition. This exercise involves pooling results of relevant published Randomized Controlled Trials (RCTs) to obtain the totality of evidence on specified outcomes of interest. This chapter mainly focuses on meta-analysis of intervention studies. The chapter aims at introducing the following topics in meta-analysis:

  1. 1.

    Statistical methods behind meta-analysis including measures of disease occurrence (e.g., odds ratio [OR], relative risk [RR], and mean difference [MD]); methods for pooling results (e.g., Peto OR, Mantel Haenszel [MH] Statistic and Inverse Variance [IV]); some study designs in clinical trials; measures of heterogeneity; and subgroup analysis

  2. 2.

    Steps involved in meta-analysis of interventions using an open-source software (R statistical software for construction of forest plots and funnel plots, and computation of heterogeneity indices)

  3. 3.

    Meta-regression including illustrative examples using R codes to demonstrate how meta-analysis is conducted for continuous and dichotomous outcomes

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Acknowledgements

The R package “meta” and the help resources (data and functions) from the meta package have largely been used to develop information on the meta-analysis section. You may also refer to the “meta” package manual for additional information on conducting meta-analysis as illustrated in this chapter.

This work was supported through the DELTAS Africa Initiative Grant No. 107754/Z/15/Z-DELTAS Africa SSACAB. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (Grant No. 107754/Z/15/Z) and the UK government. The views expressed in this publication are those of the author(s) and not necessarily those of AAS, NEPAD Agency, Wellcome Trust or the UK government.

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Authors and Affiliations

  1. School of Mathematics, University of Nairobi, Nairobi, Kenya

    Nelson Owuor Onyango

  2. African Population and Health Research Center, Nairobi, Kenya

    Hesborn Otieno Wao

Authors
  1. Nelson Owuor Onyango
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  2. Hesborn Otieno Wao
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Correspondence to Nelson Owuor Onyango .

Editor information

Editors and Affiliations

  1. College of Health Solutions, Arizona State University, Phoenix, AZ, USA

    Ding-Geng (Din) Chen

  2. Department of Statistics, University of Pretoria, Pretoria, South Africa

    Samuel O. M. Manda

  3. School of Public Health, Division of Epidemiology and Biostatistics, University of the Witwatersrand, Johannesburg, South Africa

    Tobias F. Chirwa

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Onyango, N.O., Wao, H.O. (2022). Meta-Analysis Using R Statistical Software. In: Chen, DG.(., Manda, S.O.M., Chirwa, T.F. (eds) Modern Biostatistical Methods for Evidence-Based Global Health Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-031-11012-2_7

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