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

Multilevel Metamodels: A Novel Approach to Enhance Efficiency and Generalizability in Monte Carlo Simulation Studies