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Meta-Analysis of Rare Binary Adverse Event Data

Author

Listed:
  • Dulal K. Bhaumik
  • Anup Amatya
  • Sharon-Lise T. Normand
  • Joel Greenhouse
  • Eloise Kaizar
  • Brian Neelon
  • Robert D. Gibbons

Abstract

We examine the use of fixed-effects and random-effects moment-based meta-analytic methods for analysis of binary adverse-event data. Special attention is paid to the case of rare adverse events that are commonly encountered in routine practice. We study estimation of model parameters and between-study heterogeneity. In addition, we examine traditional approaches to hypothesis testing of the average treatment effect and detection of the heterogeneity of treatment effect across studies. We derive three new methods, a simple (unweighted) average treatment effect estimator, a new heterogeneity estimator, and a parametric bootstrapping test for heterogeneity. We then study the statistical properties of both the traditional and the new methods via simulation. We find that in general, moment-based estimators of combined treatment effects and heterogeneity are biased and the degree of bias is proportional to the rarity of the event under study. The new methods eliminate much, but not all, of this bias. The various estimators and hypothesis testing methods are then compared and contrasted using an example dataset on treatment of stable coronary artery disease.

Suggested Citation

  • Dulal K. Bhaumik & Anup Amatya & Sharon-Lise T. Normand & Joel Greenhouse & Eloise Kaizar & Brian Neelon & Robert D. Gibbons, 2012. "Meta-Analysis of Rare Binary Adverse Event Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 555-567, June.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:555-567
    DOI: 10.1080/01621459.2012.664484
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    Cited by:

    1. Miguel-Angel Negrín-Hernández & María Martel-Escobar & Francisco-José Vázquez-Polo, 2021. "Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models," IJERPH, MDPI, vol. 18(2), pages 1-18, January.
    2. Soohyun Ahn & Xinlei Wang & Mumu Wang & Johan Lim, 2022. "On continuity correction for RSS-structured cluster randomized designs with binary outcomes," METRON, Springer;Sapienza Università di Roma, vol. 80(3), pages 383-397, December.
    3. Dankmar Böhning & Heinz Holling & Walailuck Böhning & Patarawan Sangnawakij, 2021. "Investigating heterogeneity in meta-analysis of studies with rare events," METRON, Springer;Sapienza Università di Roma, vol. 79(3), pages 259-272, December.
    4. Francisco-José Vázquez-Polo & Miguel-Ángel Negrín-Hernández & María Martel-Escobar, 2020. "Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach," Mathematics, MDPI, vol. 8(12), pages 1-13, December.

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