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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

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  • Michelle Tew

    (Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia)

  • Michael Willis

    (The Swedish Institute for Health Economics, Lund, Sweden)

  • Christian Asseburg

    (ESiOR Oy, Kuopio, Finland)

  • 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
    Groningen University, UMCG, Groningen, The Netherlands
    Netherlands Institute for Public Health and the Environment (RIVM), Bilthoven, 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
    Menzies Institute for Medical Research, The University of Tasmania, Hobart, Tasmania, 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
    The George Institute for Global Health, UNSW Sydney, Kensington, 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)

  • 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
    Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK)

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.

Suggested Citation

  • 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 , 2022. "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 Chal," Medical Decision Making, , vol. 42(5), pages 599-611, July.
  • Handle: RePEc:sae:medema:v:42:y:2022:i:5:p:599-611
    DOI: 10.1177/0272989X211065479
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    References listed on IDEAS

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    1. Andrew Briggs & Mark Sculpher & Martin Buxton, 1994. "Uncertainty in the economic evaluation of health care technologies: The role of sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 3(2), pages 95-104, March.
    2. Bas Groot Koerkamp & Milton C. Weinstein & Theo Stijnen & M.H. Heijenbrok-Kal & M.G. Myriam Hunink, 2010. "Uncertainty and Patient Heterogeneity in Medical Decision Models," Medical Decision Making, , vol. 30(2), pages 194-205, March.
    3. M. Brisson & W. J. Edmunds, 2006. "Impact of Model, Methodological, and Parameter Uncertainty in the Economic Analysis of Vaccination Programs," Medical Decision Making, , vol. 26(5), pages 434-446, September.
    4. Philip Clarke & Alastair Gray & Rury Holman, 2002. "Estimating Utility Values for Health States of Type 2 Diabetic Patients Using the EQ-5D (UKPDS 62)," Medical Decision Making, , vol. 22(4), pages 340-349, August.
    5. Hossein Haji Ali Afzali & Laura Bojke & Jonathan Karnon, 2020. "Improving Decision-Making Processes in Health: Is It Time for (Disease-Specific) Reference Models?," Applied Health Economics and Health Policy, Springer, vol. 18(1), pages 1-4, February.
    6. Tessa Peasgood & Alan Brennan & Peter Mansell & Jackie Elliott & Hasan Basarir & Jen Kruger, 2016. "The Impact of Diabetes-Related Complications on Preference-Based Measures of Health-Related Quality of Life in Adults with Type I Diabetes," Medical Decision Making, , vol. 36(8), pages 1020-1033, November.
    7. Lois G. Kim & Simon G. Thompson, 2010. "Uncertainty and validation of health economic decision models," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 43-55, January.
    8. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
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