ArticleMedical decision making : an international journal of the Society for Medical Decision Making2025
Meta-Modeling as a Variance-Reduction Technique for Stochastic Model-Based Cost-Effectiveness Analyses.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
PurposeWhen using stochastic models for cost-effectiveness analysis (CEA), run-to-run outcome variability arising from model stochasticity can sometimes exceed the change in outcomes resulting from an intervention, especially when individual-level efficacy is small, leading to counterintuitive results. This issue is compounded for probabilistic sensitivity analyses (PSAs), in which stochastic noise can obscure the influence of parameter uncertainty. This study evaluates meta-modeling as a variance-reduction technique to mitigate stochastic noise while preserving parameter uncertainty in PSAs.MethodsWe applied meta-modeling to 2 simulation models: 1) a 4-state Sick-Sicker model and 2) an agent-based HIV transmission model among men who have sex with men (MSM). We conducted a PSA and applied 3 meta-modeling techniques-linear regression, generalized additive models, and artificial neural networks-to reduce stochastic noise. Model performance was assessed using
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