Inference using an exact distribution of test statistic for random-effects meta-analysis
Inference using an exact distribution of test statistic for random-effects meta-analysis
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DOI：
10.1007/s10463-022-00844-4
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发表时间：
2022-07
影响因子：
1
通讯作者：
Keisuke Hanada;Tomoyuki Sugimoto
中科院分区：
文献类型：
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作者：
Keisuke Hanada;Tomoyuki Sugimoto
随机效应荟萃分析旨在将多项研究的结果与适当提出的矩估计和似然估计等方法相结合。这些现有方法基于研究数量的渐近正态性。然而，在整合少量研究时，检验和区间估计偏离名义显着性水平。尽管最近提出了一种构建更保守区间的方法，但总体治疗效果的检验统计量的确切分布尚不清楚。在本文中，我们提供了随机效应荟萃分析中检验统计量的几乎精确分布，并提出使用几乎精确分布的检验和区间估计。模拟证明了使用此处提出的方法进行估计和应用于现有荟萃分析的准确性。在已知方差参数的情况下，无论研究数量和异质性如何，使用几乎精确分布的估计性能始终达到名义显着性水平。我们还提出了一些方法来构建保守的区间估计，即使方差参数未知，并通过模拟和阿尔茨海默病荟萃分析的应用来展示它们的性能。
Random-effects meta-analysis serves to integrate the results of multiple studies with methods such as moment estimation and likelihood estimation duly proposed. These existing methods are based on asymptotic normality with respect to the number of studies. However, the test and interval estimation deviate from the nominal significance level when integrating a small number of studies. Although a method for constructing more conservative intervals has been recently proposed, the exact distribution of test statistic for the overall treatment effect is not well known. In this paper, we provide an almost-exact distribution of the test statistic in random-effects meta-analysis and propose the test and interval estimation using the almost-exact distribution. Simulations demonstrate the accuracy of estimation and application to existing meta-analysis using the method proposed here. With known variance parameters, the estimation performance using the almost-exact distribution always achieves the nominal significance level regardless of the number of studies and heterogeneity. We also propose some methods to construct a conservative interval estimation, even when the variance parameters are unknown, and present their performances via simulation and an application to Alzheimer’s disease meta-analysis.