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Longitudinal Meta-Analysis of Multiple Effect Sizes

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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

Meta-analysis methods for univariate effect sizes are well-known and developed. However, multiple outcomes are increasingly being measured and reported in medical research studies, which may lead to multiple effect sizes being estimated. The estimated effect sizes could be correlated because they are measured from the same studies. Additionally, the outcomes are often measured longitudinally, resulting in multiple effect sizes estimated repeatedly over time. Thus, the estimated effect sizes could be correlated within studies both cross-sectionally and serially due to the repeated estimation of the same effect over time in the same study. This results into longitudinal multiple effect sizes. This chapter proposes methods for statistical meta-analysis combining summary data from more than one longitudinal study with multiple effect sizes. The proposed methods are illustrated by an analysis of an example involving longitudinal meta-analysis of HIV studies assessing the effect of some antiretroviral drugs in improving viral load suppression and increasing CD4 count at weeks 4, 8, 12, 16, 20, 24, 32, 40, and 48 after start of treatment assignment.

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Acknowledgements

This chapter is part of a PhD in Statistics entitled “Meta-analysis of longitudinal studies with multiple effect sizes” by one of the chapter authors (Alfred Musekiwa). The full PhD thesis can be accessed from the University of KwaZulu-Natal Library.

Author information

Authors and Affiliations

  1. School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa

    Alfred Musekiwa

  2. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Alfred Musekiwa

  3. Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    Alfred Musekiwa

  4. Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa

    Samuel O. M. Manda

  5. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Henry G. Mwambi

  6. Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa

    Ding-Geng (Din) Chen

  7. Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA

    Samuel A. Abariga

  8. Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    Michael McCaul

  9. Centre for Evidence-based Health Care, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    Michael McCaul

  10. South African GRADE Network, Stellenbosch, South Africa

    Michael McCaul

  11. South African GRADE Network, Stellenbosch, South Africa

    Eleanor Ochodo

  12. Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya

    Eleanor Ochodo

  13. Centre for Evidence-based Health Care, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    Anke Rohwer

Authors
  1. Alfred Musekiwa
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  2. Samuel O. M. Manda
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  3. Henry G. Mwambi
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  4. Ding-Geng (Din) Chen
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  5. Samuel A. Abariga
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  6. Michael McCaul
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  7. Eleanor Ochodo
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  8. Anke Rohwer
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Corresponding author

Correspondence to Alfred Musekiwa .

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

Appendix: SAS Code

Appendix: SAS Code

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Musekiwa, A. et al. (2022). Longitudinal Meta-Analysis of Multiple Effect Sizes. 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_8

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