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Robust metrics and sensitivity analyses for meta-analyses of heterogeneous effects

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  • Mathur, Maya B
  • VanderWeele, Tyler

Abstract

We recently suggested new statistical metrics for routine reporting in random-effects meta-analyses to convey evidence strength for scientifically meaningful effects under effect heterogeneity. First, given a chosen threshold of meaningful effect size, we suggested reporting the estimated proportion of true effect sizes above this threshold. Second, we suggested reporting the proportion of effect sizes below a second, possibly symmetric, threshold in the opposite direction from the estimated mean. Our previous methods applied when the true effects are approximately normal, when the number of studies is relatively large, and when the proportion is between approximately 0.15 and 0.85. Here, we additionally describe robust methods for point estimation and inference that perform well under considerably more general conditions, as we validate in an extensive simulation study. The methods are implemented in the R package MetaUtility (function prop_stronger). We describe application of the robust methods to conducting sensitivity analyses for unmeasured confounding in meta-analyses.

Suggested Citation

  • Mathur, Maya B & VanderWeele, Tyler, 2020. "Robust metrics and sensitivity analyses for meta-analyses of heterogeneous effects," OSF Preprints r2s78, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:r2s78
    DOI: 10.31219/osf.io/r2s78
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    References listed on IDEAS

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    1. Kevin Croke & Joan Hamory Hicks & Eric Hsu & Michael Kremer & Ricardo Maertens & Edward Miguel & Witold Więcek, 2016. "Meta-Analysis and Public Policy: Reconciling the Evidence on Deworming," NBER Working Papers 22382, National Bureau of Economic Research, Inc.
    2. Croke,Kevin & Hicks,Joan Hamory & Hsu,Eric & Kremer,Michael Robert & Miguel,Edward A., 2016. "Does mass deworming affect child nutrition ? meta-analysis, cost-effectiveness, and statistical power," Policy Research Working Paper Series 7921, The World Bank.
    3. Jeffrey C. Valentine & Therese D. Pigott & Hannah R. Rothstein, 2010. "How Many Studies Do You Need?," Journal of Educational and Behavioral Statistics, , vol. 35(2), pages 215-247, April.
    4. Mathur, Maya B & VanderWeele, Tyler, 2018. "New metrics for meta-analyses of heterogeneous effects," OSF Preprints v37j6, Center for Open Science.
    5. Muhammad Farhan Majid & Su Jin Kang & Peter J Hotez, 2019. "Resolving "worm wars": An extended comparison review of findings from key economics and epidemiological studies," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 13(3), pages 1-10, March.
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    Cited by:

    1. 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.

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