Meta-analysis Using Flexible Random-effects Distribution Models
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- Noma Hisashi
- Department of Data Science, The Institute of Statistical Mathematics
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- Nagashima Kengo
- Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics
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- Kato Shogo
- Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics
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- Teramukai Satoshi
- Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
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- Furukawa Toshi A.
- Departments of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health
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説明
<p>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.</p><p>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.</p><p>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.</p><p>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.</p>
収録刊行物
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- Ｊｏｕｒｎａｌ ｏｆ Ｅｐｉｄｅｍｉｏｌｏｇｙ
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Ｊｏｕｒｎａｌ ｏｆ Ｅｐｉｄｅｍｉｏｌｏｇｙ 32 (10), 441-448, 2022-10-05
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