Missing outcome data can invalidate the results of randomized trials and their meta-analysis. However, addressing missing data is often a challenging issue because it requires untestable assumptions. The impact of missing outcome data on the meta-analysis summary effect can be explored by assuming a relationship between the outcome in the observed and the missing participants via an informative missingness parameter. The informative missingness parameters cannot be estimated from the observed data, but they can be specified, with associated uncertainty, using evidence external to the meta-analysis, such as expert opinion. The use of informative missingness parameters in pairwise meta-analysis of aggregate data with binary outcomes has been previously implemented in Stata by the metamiss command. In this article, we present the new command metamiss2, which is an extension of metamiss for binary or continuous data in pairwise or network meta-analysis. The command can be used to explore the robustness of results to different assumptions about the missing data via sensitivity analysis.

中文翻译：

---

在具有连续或二元结果的聚合数据元分析中允许信息缺失：对 metamiss 的扩展。

缺少结果数据会使随机试验及其荟萃分析的结果无效。然而，解决缺失数据通常是一个具有挑战性的问题，因为它需要不可测试的假设。缺失结果数据对荟萃分析总结效应的影响可以通过通过信息缺失参数假设观察结果与缺失参与者之间的关系来探索。无法从观察到的数据中估计信息缺失参数，但可以使用元分析之外的证据（例如专家意见）来指定它们，并具有相关的不确定性。在具有二进制结果的聚合数据的成对元分析中使用信息缺失参数已在 Stata 中通过 metamiss 命令实现。在这篇文章中，我们提出了新的命令 metamiss2，它是 metamiss 的扩展，用于成对或网络元分析中的二进制或连续数据。该命令可用于通过敏感性分析探索结果对有关缺失数据的不同假设的稳健性。