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Background Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. Methods Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. Results Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (−0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models ( P = 0.049). Conclusions Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions. Highlights The findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs). There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Background. There is growing evidence that quality of life (QoL) has a strong association with mortality. However, incorporation of QoL is uncommon in standard survival modeling. Methods. Using data extracted from a registry of patients undergoing total knee replacement (TKR), the impact of incorporating QoL in survival modeling was explored using 4 parametric survival models. QoL was incorporated and tested in 2 forms, which are baseline and change in QoL due to intervention. Life expectancy and quality-adjusted life years (QALYs) were calculated and comparisons made to a reference model (no QoL) to translate the findings in the context of modeled economic evaluations. Results. A total of 2858 TKR cases (2309 patients) who had TKR between 2006 and 2015 were included in this analysis. Increases in baseline and change in QoL were associated with a reduction in mortality. Compared to the reference model, differences of up to 0.32 life years and 0.53 QALYs were observed, and these translated into a 9.5% change in incremental effectiveness. These differences were much larger as the strength of the association between QoL and mortality increased. Conclusions. This work has demonstrated that the inclusion of QoL measures (at baseline and change from baseline) when extrapolating survival does matter. It can influence health outcomes such as life expectancy and QALYs, which are relevant in cost-effectiveness analysis. This is important because neglecting the correlation between QoL and mortality can lead to imprecise extrapolations and thus risk misleading results affecting subsequent decisions made by policy makers.