  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.
