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

Takahiro Hasegawa, Brian Claggett (), Lu Tian, Scott D. Solomon, Marc A. Pfeffer and Lee-Jen Wei ()
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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

Statistics in Biosciences, 2017, vol. 9, issue 1, No 15, 284-297

Abstract: 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.

Keywords: Meta-analysis; Clinical trials; Mixture model; Mixture population; Asymptotic normal; Covariate distribution (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12561-016-9179-3

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