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：轨迹图在元分析中很少使用，但它是一种信息量极为丰富的图形。在本文中，我们定义并阐述轨迹图的概念，并讨论其重要性。该图的贝叶斯版本将研究间标准差τ的后验密度与作为τ函数的收缩研究效应估计相结合。当研究数量较少或中等时，τ的估计精度不高，参数估计和收缩研究效应估计可能因τ的真实值不同而产生较大差异。轨迹图不仅能直观展示对τ的敏感性，还能显示出哪些τ值合理、哪些不合理。类似的可比频率学派或经验贝叶斯版本也能提供相似结果。本文通过元分析和元回归中的实例说明这些概念；在R语言中，可分别使用bayesmeta和metafor包在贝叶斯或频率学派框架下实现。