文章基本信息

标题：Meta-analysis Using Flexible Random-effects Distribution Models
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作者：Hisashi Noma ; Kengo Nagashima ; Shogo Kato 等
期刊名称：Journal of Epidemiology
印刷版ISSN：0917-5040
电子版ISSN：1349-9092
出版年度：2022
卷号：32
期号：10
页码：441-448
DOI：10.2188/jea.JE20200376
语种：English
出版社：Japan Epidemiological Association
摘要：Background In meta-analysis, the normal distribution assumption has been adopted in most systematic reviews of random-effects distribution models due to its computational and conceptual simplicity. However, this restrictive model assumption is possibly unsuitable and might have serious influences in practices. Methods We provide two examples of real-world evidence that clearly show that the normal distribution assumption is explicitly unsuitable. We propose new random-effects meta-analysis methods using five flexible random-effects distribution models that can flexibly regulate skewness, kurtosis and tailweight: skew normal distribution, skew t-distribution, asymmetric Subbotin distribution, Jones–Faddy distribution, and sinh–arcsinh distribution. We also developed a statistical package, flexmeta, that can easily perform these methods. Results Using the flexible random-effects distribution models, the results of the two meta-analyses were markedly altered, potentially influencing the overall conclusions of these systematic reviews. Conclusion The restrictive normal distribution assumption in the random-effects model can yield misleading conclusions. The proposed flexible methods can provide more precise conclusions in systematic reviews.
关键词：Key words:enmeta-analysisrandom-effects modelflexible probability distributionmodel inadequacypredictive distribution