用于荟萃分析的灵活随机效应分布模型
Flexible random-effects distribution models for meta-analysis
摘要：
在荟萃分析中，随机效应模型是解决证据综合分析中研究间异质性的标准工具。对于随机效应分布模型，正态分布模型因其计算和概念简单而被大多数系统评价采用。然而，在实践中，限制性模型假设可能会严重影响总体结论。在这篇文章中，我们首先提供了两个真实世界证据的例子，清楚地表明正态分布假设是不合适的。为了解决模型限制问题，我们提出了其他灵活的随机效应模型，可以灵活地调节偏度，峰度和尾重：偏正态分布，偏t分布，非对称Subbotin分布，琼斯-法迪分布，和sinh-arcsinh分布。我们还开发了一个R包flexmeta，它可以轻松地执行这些方法。使用灵活的随机效应分布模型，两项荟萃分析的结果发生了显著变化，可能影响这些系统评价的总体结论。灵活的方法和计算工具可以提供更精确的证据，这些方法至少可以作为敏感性分析工具来评估随机效应模型的正态分布假设的影响。
英文摘要：
In meta-analysis, the random-effects models are standard tools to address between-study heterogeneity in evidence synthesis analyses. For the random-effects distribution models, the normal distribution model has been adopted in most systematic reviews due to its computational and conceptual simplicity. However, the restrictive model assumption might have serious influences on the overall conclusions in practices. In this article, we first provide two examples of real-world evidence that clearly show that the normal distribution assumption is unsuitable. To address the model restriction problem, we propose alternative flexible random-effects 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 R package, flexmeta, that can easily perform these methods. 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. The flexible methods and computational tools can provide more precise evidence, and these methods would be recommended at least as sensitivity analysis tools to assess the influence of the normal distribution assumption of the random-effects model.
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