Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies
Methodology
2025-11-21 v4 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2401.07294,
title = {Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies},
author = {Joshua Gilbert and Luke Miratrix},
journal= {arXiv preprint arXiv:2401.07294},
year = {2025}
}