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A re‐evaluation of fixed effect(s) meta‐analysis

Author

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  • Kenneth Rice
  • Julian P. T. Higgins
  • Thomas Lumley

Abstract

Meta‐analysis is a common tool for synthesizing results of multiple studies. Among methods for performing meta‐analysis, the approach known as ‘fixed effects’ or ‘inverse variance weighting’ is popular and widely used. A common interpretation of this method is that it assumes that the underlying effects in contributing studies are identical, and for this reason it is sometimes dismissed by practitioners. However, other interpretations of fixed effects analyses do not make this assumption, yet appear to be little known in the literature. We review these alternative interpretations, describing both their strengths and their limitations. We also describe how heterogeneity of the underlying effects can be addressed, with the same minimal assumptions, through either testing or meta‐regression. Recommendations for the practice of meta‐analysis are given; it is hoped that these will foster more direct connection of the questions that meta‐analysts wish to answer with the statistical methods they choose.

Suggested Citation

  • Kenneth Rice & Julian P. T. Higgins & Thomas Lumley, 2018. "A re‐evaluation of fixed effect(s) meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 205-227, January.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:1:p:205-227
    DOI: 10.1111/rssa.12275
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    Cited by:

    1. Ms. Juliana Dutra Araujo & Manasa Patnam & Ms. Adina Popescu & Mr. Fabian Valencia & Weijia Yao, 2020. "Effects of Macroprudential Policy: Evidence from Over 6,000 Estimates," IMF Working Papers 2020/067, International Monetary Fund.
    2. Leire Erkoreka & Naiara Ozamiz-Etxebarria & Onintze Ruiz & Javier Ballesteros, 2020. "Assessment of Psychiatric Symptomatology in Bilingual Psychotic Patients: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 17(11), pages 1-11, June.
    3. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.
    4. Islas-Aguirre, Juan Francisco & Venegas-Martínez, Francisco, 2024. "Interacción entre apoyos monetarios agrícolas y pobreza en los estados de mayor producción agrícola en México (2020), un enfoque de econometría espacial," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 19(40), pages 203-231, Primer se.
    5. Longford Nicholas T., 2021. "Unreported standard errors in meta-analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 22(4), pages 1-17, December.
    6. Beck, Günter W. & Lein, Sarah M., 2020. "Price elasticities and demand-side real rigidities in micro data and in macro models," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 200-212.
    7. Nicholas T. Longford, 2021. "Unreported standard errors in meta-analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 22(4), pages 1-17, December.
    8. Bhatia, Madhur & Gulati, Rachita, 2021. "Board governance and bank performance: A meta- analysis," Research in International Business and Finance, Elsevier, vol. 58(C).
    9. Sacha Epskamp & Adela-Maria Isvoranu & Mike W.-L. Cheung, 2022. "Meta-analytic Gaussian Network Aggregation," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 12-46, March.

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