Examining the impact of structural uncertainty across ten type 2 diabetes models: Results from the 2022 Mount Hood Challenge.

OBJECTIVE: The Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes. METHODS: Ten independent modelling groups completed a blinded simulation exercise to estimate the cost-effectiveness of three interventions in two type 2 diabetes populations. Modelling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB), and the drivers of those differences. RESULTS: Overall, modelling groups agreed which interventions had a positive NMB (i.e. were cost-effective), though estimates of NMB varied substantially- by up to £23,696 for one intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modelled health states was also a significant predictor of NMB. CONCLUSIONS: This exercise demonstrates that structural uncertainty between different health economic models impacts cost-effectiveness estimates. Whilst it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimising the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modelling.