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Methods to address confounding and other biases in meta-analyses: Review and recommendations

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  • Mathur, Maya B
  • VanderWeele, Tyler

Abstract

Meta-analyses contribute critically to cumulative science, but they can produce misleading conclusions if their constituent primary studies are biased, for example by unmeasured confounding in nonrandomized studies. We provide practical guidance on how meta-analysts can address confounding and other biases that affect studies' internal validity, focusing primarily on sensitivity analyses that help quantify how biased the meta-analysis estimates might be. We review a number of sensitivity analysis methods to do so, especially recent developments that are straightforward to implement and interpret and that use somewhat less stringent statistical assumptions than earlier methods. We give recommendations for how these methods could be applied in practice and illustrate using a previously published meta-analysis. Sensitivity analyses can provide informative quantitative summaries of evidence strength, and we suggest reporting them routinely in meta-analyses of potentially biased studies. This recommendation in no way diminishes the importance of defining study eligibility criteria that reduce bias and of characterizing studies’ risks of bias qualitatively.

Suggested Citation

  • Mathur, Maya B & VanderWeele, Tyler, 2021. "Methods to address confounding and other biases in meta-analyses: Review and recommendations," OSF Preprints v7dtq, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:v7dtq
    DOI: 10.31219/osf.io/v7dtq
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    References listed on IDEAS

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    1. Shadish, William R. & Clark, M. H. & Steiner, Peter M., 2008. "Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1334-1344.
    2. Su Golder & Yoon K Loke & Martin Bland, 2011. "Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview," PLOS Medicine, Public Library of Science, vol. 8(5), pages 1-13, May.
    3. Mathur, Maya B & VanderWeele, Tyler, 2020. "Robust metrics and sensitivity analyses for meta-analyses of heterogeneous effects," OSF Preprints r2s78, Center for Open Science.
    4. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    5. Maya B. Mathur & Tyler J. VanderWeele, 2020. "Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 163-172, January.
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