Simulation studies are commonly used in methodological research for the empirical evaluation of data analysis methods. They generate artificial data sets under specified mechanisms and compare the performance of methods across conditions. However, simulation repetitions do not always produce valid outputs, e.g., due to non-convergence or other algorithmic failures. This phenomenon complicates the interpretation of results, especially when its occurrence differs between methods and conditions. Despite the potentially serious consequences of such "missingness", quantitative data on its prevalence and specific guidance on how to deal with it are currently limited. To this end, we reviewed 482 simulation studies published in various methodological journals and systematically assessed the prevalence and handling of missingness. We found that only 23.0% (111/482) of the reviewed simulation studies mention missingness, with even fewer reporting frequency (92/482 = 19.1%) or how it was handled (67/482 = 13.9%). We propose a classification of missingness and possible solutions. We give various recommendations, most notably to always quantify and report missingness, even if none was observed, to align missingness handling with study goals, and to share code and data for reproduction and reanalysis. Using a case study on publication bias adjustment methods, we illustrate common pitfalls and solutions.

翻译：仿真研究在方法学研究中常用于数据分析方法的实证评估。此类研究在特定机制下生成人工数据集，并比较不同条件下方法的性能。然而，仿真重复并不总能产生有效输出，例如因算法不收敛或其他故障导致。这种现象使结果解释复杂化，尤其当其发生频率因方法和条件而异时。尽管此类“缺失”可能带来严重后果，目前关于其普遍性的量化数据及具体处理指南仍十分有限。为此，我们回顾了发表于各方法学期刊的482项仿真研究，系统评估了缺失现象的普遍性及处理方式。研究发现仅23.0%（111/482）的仿真研究提及缺失现象，报告缺失频率（92/482 = 19.1%）或处理方式（67/482 = 13.9%）的研究更少。我们提出了缺失现象的分类体系及可能的解决方案，并给出多项建议，主要包括：始终量化并报告缺失情况（即使未观测到缺失），使缺失处理与研究目标保持一致，以及共享代码和数据以支持复现与再分析。通过一个关于发表偏倚调整方法的案例研究，我们阐明了常见误区及其解决方案。