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：轨迹图在元分析中鲜少使用，但它是一种信息量极大的可视化工具。本文旨在定义并阐释轨迹图的概念，探讨其重要性。该图的贝叶斯版本结合了τ（即研究间标准差）的后验密度与作为τ函数的收缩研究效应估计值。当研究数量较少或中等时，τ的估计精度有限，参数估计和收缩研究效应估计值会因τ的实际取值不同而产生显著差异。轨迹图既能直观展示对τ的敏感性，又能显示τ的取值哪些是合理的、哪些是不合理的。与之类似，频率学派或经验贝叶斯版本也能提供相同的结果。本文通过元分析和元回归案例说明相关概念；在贝叶斯或频率学派框架下，可分别使用R语言中的bayesmeta和metafor包实现上述分析。