ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022
Metamodeling for Policy Simulations with Multivariate Outcomes.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Meta-Modeling as a Variance-Reduction Technique for Stochastic Model-Based Cost-Effectiveness Analyses.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Emulator-based Bayesian calibration of the CISNET colorectal cancer models.medRxiv : the preprint server for health sciences · 2024Article
- The application of artificial intelligence in health policy: a scoping review.BMC health services research · 2023Article
- When Is Mass Prophylaxis Cost-Effective for Epidemic Control? A Comparison of Decision Approaches.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
purposeMetamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes.
methodsWe combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings.
resultsOutput variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by
conclusionsIn our example application, the choice of base learner had the largest impact on
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.