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The Myth of Making Inferences for an Overall Treatment Efficacy with Data from Multiple Comparative Studies Via Meta-Analysis

Author

Listed:
  • Takahiro Hasegawa

    (Shionogi & Co., Ltd.)

  • Brian Claggett

    (Brigham and Women’s Hospital)

  • Lu Tian

    (Stanford University School of Medicine)

  • Scott D. Solomon

    (Brigham and Women’s Hospital)

  • Marc A. Pfeffer

    (Brigham and Women’s Hospital)

  • Lee-Jen Wei

    (Harvard University)

Abstract

Meta-analysis techniques, if applied appropriately, can provide a summary of the totality of evidence regarding an overall difference between a new treatment and a control group using data from multiple comparative clinical studies. The standard meta-analysis procedures, however, may not give a meaningful between-group difference summary measure or identify a meaningful patient population of interest, especially when the fixed-effect model assumption is not met. Moreover, a single between-group comparison measure without a reference value obtained from patients in the control arm would likely not be informative enough for clinical decision making. In this paper, we propose a simple, robust procedure based on a mixture population concept and provide a clinically meaningful group contrast summary for a well-defined target population. We use the data from a recent meta-analysis for evaluating statin therapies with respect to the incidence of fatal stroke events to illustrate the issues associated with the standard meta-analysis procedures as well as the advantages of our simple proposal.

Suggested Citation

  • Takahiro Hasegawa & Brian Claggett & Lu Tian & Scott D. Solomon & Marc A. Pfeffer & Lee-Jen Wei, 2017. "The Myth of Making Inferences for an Overall Treatment Efficacy with Data from Multiple Comparative Studies Via Meta-Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 284-297, June.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9179-3
    DOI: 10.1007/s12561-016-9179-3
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    References listed on IDEAS

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    1. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
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    Cited by:

    1. Li, Ting & Shi, Chengchun & Wen, Qianglin & Sui, Yang & Qin, Yongli & Lai, Chunbo & Zhu, Hongtu, 2024. "Combining experimental and historical data for policy evaluation," LSE Research Online Documents on Economics 125588, London School of Economics and Political Science, LSE Library.

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