Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge |
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| Authors: | Andrew J Palmer Lei Si Michelle Tew Xinyang Hua Michael S Willis Christian Asseburg Phil McEwan José Leal Alastair Gray Volker Foos Mark Lamotte Talitha Feenstra Patrick J O’Connor Michael Brandle Harry J Smolen James C Gahn William J Valentine Richard F Pollock Philip M Clarke |
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| Institution: | 1. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia;2. Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia;3. The Swedish Institute for Health Economics, Lund, Sweden;4. Health Economics and Outcomes Research Ltd., Cardiff, UK;5. Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK;6. IQVIA, Real-World Evidence Solutions, Zaventem, Belgium;7. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands;8. Groningen University, University Medical Center Groningen, Groningen, The Netherlands;9. HealthPartners Institute and HealthPartners Center for Chronic Care Innovation, Minneapolis, MN, USA;10. Department of Internal Medicine, Kantonsspital St. Gallen, St. Gallen, Switzerland;11. Medical Decision Modeling Inc., Indianapolis, IN, USA;12. Ossian Health Economics and Communications, Basel, Switzerland;13. School of Health and Related Research, University of Sheffield, Sheffield, UK;14. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA;15. Departments of Internal Medicine and Epidemiology, University of Michigan, Ann Arbor, MI, USA;16. Departments of Internal Medicine, University of Michigan, Ann Arbor, MI, USA;17. Department of Medicine, University of Chicago, Chicago, IL, USA |
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| Abstract: | ObjectivesThe Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.MethodsParticipating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed.ResultsSeven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed.ConclusionsTransparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results. |
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| Keywords: | computer modeling diabetes Mount Hood Challenge transparency |
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