The prediction interval has been increasingly used in meta-analyses as a useful measure for assessing the magnitude of treatment effect and between-studies heterogeneity. In calculations of the prediction interval, although the Higgins-Thompson-Spiegelhalter method is used most often in practice, it might not have adequate coverage probability for the true treatment effect of a future study under realistic situations. An effective alternative candidate is the Bayesian prediction interval, which has also been widely used in general prediction problems. However, these prediction intervals are constructed based on the Bayesian philosophy, and their frequentist validities are only justified by large-sample approximations even if noninformative priors are adopted. There has been no certain evidence that evaluated their frequentist performances under realistic situations of meta-analyses. In this study, we conducted extensive simulation studies to assess the frequentist coverage performances of Bayesian prediction intervals with 11 noninformative prior distributions under general meta-analysis settings. Through these simulation studies, we found that frequentist coverage performances strongly depended on what prior distributions were adopted. In addition, when the number of studies was smaller than 10, there were no prior distributions that retained accurate frequentist coverage properties. We also illustrated these methods via applications to two real meta-analysis datasets. The resultant prediction intervals also differed according to the adopted prior distributions. Inaccurate prediction intervals may provide invalid evidence and misleading conclusions. Thus, if frequentist accuracy is required, Bayesian prediction intervals should be used cautiously in practice.

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用于随机效应荟萃分析的贝叶斯预测区间的频率表现

预测区间越来越多地用于荟萃分析，作为评估治疗效果大小和研究间异质性的有用指标。在预测区间的计算中，虽然 Higgins-Thompson-Spiegelhalter 方法在实践中最常用，但对于未来研究在现实情况下的真实治疗效果，它可能没有足够的覆盖概率。一个有效的替代候选者是贝叶斯预测区间，它也被广泛用于一般预测问题。然而，这些预测区间是基于贝叶斯哲学构建的，即使采用非信息性先验，它们的频率有效性也只能通过大样本近似来证明。没有确定的证据可以评估他们在元分析的现实情况下的常客表现。在这项研究中，我们进行了广泛的模拟研究，以评估在一般元分析设置下具有 11 个非信息性先验分布的贝叶斯预测区间的频率覆盖性能。通过这些模拟研究，我们发现常客覆盖性能在很大程度上取决于所采用的先验分布。此外，当研究数量小于 10 时，没有保留准确的常客覆盖属性的先验分布。我们还通过将这些方法应用于两个真实的元分析数据集来说明这些方法。根据采用的先验分布，得到的预测区间也不同。不准确的预测区间可能会提供无效的证据和误导性的结论。因此，如果需要频率精确度，在实践中应谨慎使用贝叶斯预测区间。