OBJECTIVES: Meta-models, also known as surrogate models or emulators, are used to negate runtime issues with analyzing computational demanding simulation models in several fields of research. This study aimed to create awareness of the potential of applying meta-modeling methods in health economics by providing an overview of previous meta-modeling applications in this context.

METHODS: A systematic literature search was performed on PubMed using primary search terms on meta-modeling, surrogate modeling, and emulators, combined with well-known, previously used terms on modeling, simulation, and health economics. Studies were initially assessed based on title and abstract, and subsequently based on full-text, and excluded if no meta-modeling methods were applied or if a meta-modeling study did not relate to: health economics, health logistics, or epidemiology. The sample was enriched by cross-referencing from the full text of the included publications. The final sample was analyzed to identify general study characteristics and meta-modeling specific information, such as the applied meta-modeling technique and design of experiments.

RESULTS: Included publications (n=13) used meta-models to negate runtime issues with analyzing both cohort-based and patient-level simulation models. Performed analyses using meta-models included: model calibration (n=1, 8%), deterministic sensitivity analysis (n=4, 31%), probabilistic sensitivity analysis (n=1), value of information analysis (n=5, 38%), or optimization (n=1). Another reason to apply meta-modeling methods was to extrapolate a simulation model to other countries (n=1). Applied meta-modeling techniques varied considerably between studies, with linear regression being most frequently applied (n=7, 54%).

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