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 and Philip Clarke
Additional contact information
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
James Gahn: Medical Decision Modeling Inc., Indianapolis, IN, USA
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
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

Medical Decision Making, 2022, vol. 42, issue 5, 599-611

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.

Keywords: cross-model variability; diabetes; economic model; simulation model; structural uncertainty; quality-of-life (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:42:y:2022:i:5:p:599-611

DOI: 10.1177/0272989X211065479

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