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（MCS）结果进行回归分析，为总结MCS发现提供了有力工具。然而，尚未被探索的一种方法是使用多层元模型（MLMM），其能更好地解释MCS结果中因对同一模拟数据集拟合多个模型而产生的依赖数据结构。在本研究中，我们阐明了MLMM的理论依据，并展示了它如何能显著提升传统回归方法的效率、更好地解释复杂MCS设计，并为MCS发现的泛化能力提供新的洞见。