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Simple heterogeneity variance estimation for meta‐analysis

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  • Kurex Sidik
  • Jeffrey N. Jonkman

Abstract

Summary. A simple method of estimating the heterogeneity variance in a random‐effects model for meta‐analysis is proposed. The estimator that is presented is simple and easy to calculate and has improved bias compared with the most common estimator used in random‐effects meta‐analysis, particularly when the heterogeneity variance is moderate to large. In addition, it always yields a non‐negative estimate of the heterogeneity variance, unlike some existing estimators. We find that random‐effects inference about the overall effect based on this heterogeneity variance estimator is more reliable than inference using the common estimator, in terms of coverage probability for an interval estimate.

Suggested Citation

  • Kurex Sidik & Jeffrey N. Jonkman, 2005. "Simple heterogeneity variance estimation for meta‐analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 367-384, April.
    • Handle: RePEc:bla:jorssc:v:54:y:2005:i:2:p:367-384
    DOI: 10.1111/j.1467-9876.2005.00489.x
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    Cited by:

    1. Johannes Hönekopp & Audrey Helen Linden, 2022. "Heterogeneity estimates in a biased world," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-21, February.
    2. Yu, Dalei & Ding, Chang & He, Na & Wang, Ruiwu & Zhou, Xiaohua & Shi, Lei, 2019. "Robust estimation and confidence interval in meta-regression models," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 93-118.
    3. Sidik, Kurex & Jonkman, Jeffrey N., 2006. "Robust variance estimation for random effects meta-analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3681-3701, August.
    4. Yolanda Álvarez-Pérez & Amado Rivero-Santana & Lilisbeth Perestelo-Pérez & Andrea Duarte-Díaz & Vanesa Ramos-García & Ana Toledo-Chávarri & Alezandra Torres-Castaño & Beatriz León-Salas & Diego Infant, 2022. "Effectiveness of Mantra-Based Meditation on Mental Health: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(6), pages 1-18, March.
    5. Mathur, Maya B & VanderWeele, Tyler, 2017. "Sensitivity analysis for unmeasured confounding in meta-analyses," OSF Preprints jkhfg, Center for Open Science.
    6. Weber, Frank & Knapp, Guido & Ickstadt, Katja & Kundt, Günther & Glass, Anne, 2020. "Zero-cell corrections in random-effects meta-analyses," OSF Preprints qjh5f, Center for Open Science.
    7. Nick Drydakis, 2022. "Sexual orientation and earnings: a meta-analysis 2012–2020," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(2), pages 409-440, April.
    8. Lanju Zhang & Zailong Wang & Li Wang & Lu Cui & Jeremy Sokolove & Ivan Chan, 2022. "A Simple Approach to Incorporating Historical Control Data in Clinical Trial Design and Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 216-236, July.
    9. Weber, Frank & Knapp, Guido & Glass, Anne & Kundt, Günther & Ickstadt, Katja, 2020. "Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods," OSF Preprints 5zbh6, Center for Open Science.
    10. Heinz Holling & Walailuck Böhning & Dankmar Böhning, 2012. "Likelihood-Based Clustering of Meta-Analytic SROC Curves," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 106-126, January.
    11. Mathur, Maya B & VanderWeele, Tyler, 2018. "Statistical methods for evidence synthesis," Thesis Commons kd6ja, Center for Open Science.

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