Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.
Graduate School of Economics, Kyoto University, Kyoto, Japan.
Res Synth Methods. 2023 Nov;14(6):794-806. doi: 10.1002/jrsm.1651. Epub 2023 Jul 3.
Network meta-analysis has played an important role in evidence-based medicine for assessing the comparative effectiveness of multiple available treatments. The prediction interval has been one of the standard outputs in recent network meta-analysis as an effective measure that enables simultaneous assessment of uncertainties in treatment effects and heterogeneity among studies. To construct the prediction interval, a large-sample approximating method based on the t-distribution has generally been applied in practice; however, recent studies have shown that similar t-approximation methods for conventional pairwise meta-analyses can substantially underestimate the uncertainty under realistic situations. In this article, we performed simulation studies to assess the validity of the current standard method for network meta-analysis, and we show that its validity can also be violated under realistic situations. To address the invalidity issue, we developed two new methods to construct more accurate prediction intervals through bootstrap and Kenward-Roger-type adjustment. In simulation experiments, the two proposed methods exhibited better coverage performance and generally provided wider prediction intervals than the ordinary t-approximation. We also developed an R package, PINMA (https://cran.r-project.org/web/packages/PINMA/), to perform the proposed methods using simple commands. We illustrate the effectiveness of the proposed methods through applications to two real network meta-analyses.
网络荟萃分析在循证医学中发挥了重要作用，可用于评估多种现有治疗方法的相对疗效。预测区间是最近网络荟萃分析的标准输出之一，是一种有效的衡量标准，可以同时评估治疗效果的不确定性和研究之间的异质性。为了构建预测区间，实践中通常采用基于 t 分布的大样本逼近方法；然而，最近的研究表明，传统的两两荟萃分析的类似 t 逼近方法在实际情况下可能会大大低估不确定性。在本文中，我们进行了模拟研究，以评估网络荟萃分析当前标准方法的有效性，结果表明在实际情况下，其有效性也可能受到违反。为了解决无效性问题，我们开发了两种新的方法，通过自举和肯沃德-罗杰型调整来构建更准确的预测区间。在模拟实验中，这两种新方法表现出了更好的覆盖性能，并且通常比普通的 t 逼近提供了更宽的预测区间。我们还开发了一个 R 包 PINMA（https://cran.r-project.org/web/packages/PINMA/），可以使用简单的命令执行所提出的方法。我们通过对两个真实的网络荟萃分析的应用来说明了所提出方法的有效性。
