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追踪图如何帮助解释荟萃分析结果。

How trace plots help interpret meta-analysis results.

机构信息

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Graduate School and University Center, City University of New York, New York, New York, USA.

出版信息

Res Synth Methods. 2024 May;15(3):413-429. doi: 10.1002/jrsm.1693. Epub 2023 Dec 15.

Abstract

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of , the between-study standard deviation, and the shrunken estimates of the study effects as a function of . With a small or moderate number of studies, is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of . The trace plot allows visualization of the sensitivity to along with a plot that shows which values of are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementation in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.

摘要

折线图在荟萃分析中很少使用,但它是一种非常有用的图。在本文中,我们定义并说明了折线图是什么,并讨论了它为什么很重要。该图的贝叶斯版本将 的后验密度、研究间标准差和研究效果的收缩估计值结合在一起,作为 的函数。对于少数或中等数量的研究, 估计的精度不高,并且参数估计和收缩研究效果估计会根据 的正确值而有很大的变化。折线图允许可视化对 的敏感性,同时还显示了哪些 值是合理的,哪些 值是不合理的。类似的频率派或经验贝叶斯版本提供了类似的结果。使用荟萃分析和荟萃回归中的示例来说明这些概念;在 R 中,可以分别使用 bayesmeta 和 metafor 包在贝叶斯或频率派框架中实现。

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