Metamodels, or the regression analysis of Monte Carlo simulation (MCS) results, provide a powerful tool to summarize MCS findings. However, an as of yet unexplored approach is the use of multilevel metamodels (MLMM) that better account for the dependent data structure of MCS results that arises from fitting multiple models to the same simulated data set. In this study, we articulate the theoretical rationale for the MLMM and illustrate how it can dramatically improve efficiency over the traditional regression approach, better account for complex MCS designs, and provide new insights into the generalizability of MCS findings.

翻译：元模型（即对蒙特卡洛模拟结果的回归分析）为总结蒙特卡洛模拟研究发现提供了强有力的工具。然而，目前尚未探索的一种方法是使用多层元模型（MLMM），该模型能更好地解释蒙特卡洛模拟结果中因对同一模拟数据集拟合多个模型而产生的数据依赖结构。本研究阐述了多层元模型的理论依据，论证了其相较于传统回归方法如何显著提升效率、更精准地解释复杂蒙特卡洛模拟设计，并为蒙特卡洛模拟研究结果的普适性提供新见解。