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Estimation of Effect Heterogeneity in Rare Events Meta-Analysis

Author

Listed:
  • Heinz Holling

    (University of Münster)

  • Katrin Jansen

    (University of Münster)

  • Walailuck Böhning

    (University of Münster)

  • Dankmar Böhning

    (University of Southampton)

  • Susan Martin

    (University of Southampton)

  • Patarawan Sangnawakij

    (Thammasat University)

Abstract

The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.

Suggested Citation

  • Heinz Holling & Katrin Jansen & Walailuck Böhning & Dankmar Böhning & Susan Martin & Patarawan Sangnawakij, 2022. "Estimation of Effect Heterogeneity in Rare Events Meta-Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1081-1102, September.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:3:d:10.1007_s11336-021-09835-5
    DOI: 10.1007/s11336-021-09835-5
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    References listed on IDEAS

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    1. Philipp Doebler & Heinz Holling, 2015. "Meta-analysis of Diagnostic Accuracy and ROC Curves with Covariate Adjusted Semiparametric Mixtures," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1084-1104, December.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    3. 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.
    4. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    5. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
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