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 tau, the between-study standard deviation, and the shrunken estimates of the study effects as a function of tau. With a small or moderate number of studies, tau is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of tau. The trace plot allows visualization of the sensitivity to tau along with a plot that shows which values of tau 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; implementaton in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.

翻译：痕迹图在荟萃分析中较少使用，但它是信息量极为丰富的图表。本文定义并阐释了痕迹图的概念，探讨其重要性。该图的贝叶斯版本结合了τ（研究间标准差）的后验密度，以及随τ变化的收缩研究效应估计值。当研究数量较少或中等时，τ的估计精确度不足，参数估计和收缩研究效应值会因τ的真实值不同而产生显著差异。痕迹图不仅能展示对τ的敏感性，还能直观显示哪些τ值合理、哪些不合理。其对应的频率学派或经验贝叶斯版本可得出类似结果。本文通过荟萃分析与荟萃回归示例阐述相关概念，并分别利用R中的bayesmeta和metafor软件包在贝叶斯或频率学派框架下实现。