Meta-analysis Using Flexible Random-effects Distribution Models
| Autor: | Hisashi Noma, Kengo Nagashima, Shogo Kato, Satoshi Teramukai, Toshi A. Furukawa |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Epidemiology, Vol 32, Iss 10, Pp 441-448 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0917-5040 1349-9092 |
| DOI: | 10.2188/jea.JE20200376 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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