Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge. (July 2022)

• Record Type: Journal Article
• Authors: Tew, Michelle; Willis, Michael; Asseburg, Christian; Bennett, Hayley; Brennan, Alan; Feenstra, Talitha; Gahn, James; Gray, Alastair; Heathcote, Laura; Herman, William H.; Isaman, Deanna; Kuo, Shihchen; Lamotte, Mark; Leal, José; McEwan, Phil; Nilsson, Andreas; Palmer, Andrew J.; Patel, Rishi; Pollard, Daniel; Ramos, Mafalda; Sailer, Fabian; Schramm, Wendelin; Shao, Hui; Shi, Lizheng; Si, Lei; Smolen, Harry J.; Thomas, Chloe; Tran-Duy, An; Yang, Chunting; Ye, Wen; Yu, Xueting; Zhang, Ping; Clarke, Philip;
• Is Part Of: Medical decision making. Volume 42:Number 5(2022)
• Page Start: 599
• Page End: 611
• Publication Date: 2022-07
• Subjects: cross-model variability -- diabetes -- economic model -- simulation model -- structural uncertainty -- quality-of-life; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X211065479
• Languages: English
• ISSNs: 0272-989X

Abstract

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.