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.
Michelle Tew, Michael Willis, Christian Asseburg, Hayley Bennett, Alan Brennan, Talitha Feenstra, James Gahn, Alastair Gray, Laura Heathcote, William H Herman, Deanna Isaman, Shihchen Kuo, Mark Lamotte, José Leal, Phil McEwan, Andreas Nilsson, Andrew J Palmer, Rishi Patel, Daniel Pollard, Mafalda Ramos, Fabian Sailer, Wendelin Schramm, Hui Shao, Lizheng Shi, Lei Si, Harry J Smolen, Chloe Thomas, An Tran-Duy, Chunting Yang, Wen Ye, Xueting Yu, Ping Zhang, Philip Clarke
Author Information
Michelle Tew: Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia. ORCID
Michael Willis: The Swedish Institute for Health Economics, Lund, Sweden.
Christian Asseburg: ESiOR Oy, Kuopio, Finland. ORCID
Hayley Bennett: Health Economics and Outcomes Research Ltd, Cardiff, UK.
Alan Brennan: School of Health and Related Research, University of Sheffield, Sheffield, UK.
Talitha Feenstra: Groningen University, Faculty of Science and Engineering, GRIP, Groningen, The Netherlands.
James Gahn: Medical Decision Modeling Inc., Indianapolis, IN, USA.
Alastair Gray: Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Laura Heathcote: School of Health and Related Research, University of Sheffield, Sheffield, UK.
William H Herman: Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Deanna Isaman: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Shihchen Kuo: Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Mark Lamotte: Global Health Economics and Outcomes Research, Real World Solutions, IQVIA, Zaventem, Belgium.
José Leal: Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Phil McEwan: Health Economics and Outcomes Research Ltd, Cardiff, UK.
Andreas Nilsson: The Swedish Institute for Health Economics, Lund, Sweden.
Andrew J Palmer: Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Rishi Patel: Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Daniel Pollard: School of Health and Related Research, University of Sheffield, Sheffield, UK.
Mafalda Ramos: Global Health Economics and Outcomes Research, Real World Solutions, IQVIA, Porto Salvo, Portugal.
Fabian Sailer: GECKO Institute for Medicine, Informatics and Economics, Heilbronn University, Heilbronn, Germany.
Wendelin Schramm: GECKO Institute for Medicine, Informatics and Economics, Heilbronn University, Heilbronn, Germany.
Hui Shao: Department of Pharmaceutical Outcomes and Policy. University of Florida College of Pharmacy. Gainesville, FL, USA.
Lizheng Shi: Department of Health Policy and Management; Tulane University School of Public Health and Tropical Medicine.
Lei Si: Menzies Institute for Medical Research, The University of Tasmania, Hobart, Tasmania, Australia.
Harry J Smolen: Medical Decision Modeling Inc., Indianapolis, IN, USA.
Chloe Thomas: School of Health and Related Research, University of Sheffield, Sheffield, UK.
An Tran-Duy: Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia. ORCID
Chunting Yang: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Wen Ye: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Xueting Yu: Medical Decision Modeling Inc., Indianapolis, IN, USA.
Ping Zhang: Division of Diabetes Translation, Centres for Disease Control and Prevention, Atlanta, GA, USA.
Philip Clarke: Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
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 ( = 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.HighlightsThe 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.
