Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies

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Hauptverfasser: Gilbert, Joshua, Miratrix, Luke
Format: Preprint
Veröffentlicht: 2024
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author Gilbert, Joshua
Miratrix, Luke
author_facet Gilbert, Joshua
Miratrix, Luke
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_07294
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies
Gilbert, Joshua
Miratrix, Luke
Methodology
Statistics Theory
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.
title Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2401.07294