Metamodels, or the regression analysis of Monte Carlo simulation results, provide a powerful tool to summarize simulation findings. However, an underutilized approach is the multilevel metamodel (MLMM) that accounts for the dependent data structure 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 improve the interpretability of simulation results, better account for complex simulation designs, and provide new insights into the generalizability of simulation findings.

翻译：元模型，即对蒙特卡洛模拟结果进行回归分析，为总结模拟发现提供了有力工具。然而，多级元模型（MLMM）作为一种尚未被充分利用的方法，能够处理因对同一模拟数据集拟合多个模型而产生的相依数据结构。本研究阐明了MLMM的理论依据，并阐释了该方法如何提升模拟结果的可解释性、更好地处理复杂模拟设计，以及对模拟发现的泛化能力提供新的见解。