A scoping review of metamodeling applications and opportunities for advanced health economic analyses

Metamodels, also known as meta-models, surrogate models, or emulators, are used in several fields of research to negate runtime issues with analyzing computational demanding simulation models. This study introduces metamodeling and presents results of a review on metamodeling applications in health economics.

A scoping review was performed to identify studies that applied metamodeling methods in a health economic context. After search and selection, 13 publications were found to employ metamodeling methods in health economics. Metamodels were used to perform value of information analysis (n = 5, 38%), deterministic sensitivity analysis (n = 4, 31%), model calibration (n = 1, 8%), probabilistic sensitivity analysis (n = 1), or optimization (n = 1, 8%). One study was found to extrapolate a simulation model to other countries (n = 1, 8%). Applied metamodeling techniques varied considerably between studies, with linear regression being most frequently applied (n = 7, 54%).

Although it has great potential to enable computational demanding analyses of health economic models, metamodeling in health economics is still in its infancy, as illustrated by the limited number of applications and the relatively simple metamodeling methods applied. Comprehensive guidance specific to health economics is needed to provide modelers with the information and tools needed to utilize the full potential of metamodels.