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Model Transparency and Validation

Author

Listed:
  • David M. Eddy
  • William Hollingworth
  • J. Jaime Caro
  • Joel Tsevat
  • Kathryn M. McDonald
  • John B. Wong

Abstract

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well it reproduces reality). This report describes recommendations for achieving transparency and validation, developed by a task force appointed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM). Recommendations were developed iteratively by the authors. A nontechnical description should be made available to anyone—including model type and intended applications; funding sources; structure; inputs, outputs, other components that determine function, and their relationships; data sources; validation methods and results; and limitations. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing same problem), external validity (comparing model results to real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this paper contains a number of recommendations that were iterated among the authors, as well as the wider modeling task force jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

Suggested Citation

  • David M. Eddy & William Hollingworth & J. Jaime Caro & Joel Tsevat & Kathryn M. McDonald & John B. Wong, 2012. "Model Transparency and Validation," Medical Decision Making, , vol. 32(5), pages 733-743, September.
  • Handle: RePEc:sae:medema:v:32:y:2012:i:5:p:733-743
    DOI: 10.1177/0272989X12454579
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    18. L. Standfield & T. Comans & M. Raymer & S. O’Leary & N. Moretto & P. Scuffham, 2016. "The Efficiency of Increasing the Capacity of Physiotherapy Screening Clinics or Traditional Medical Services to Address Unmet Demand in Orthopaedic Outpatients: A Practical Application of Discrete Eve," Applied Health Economics and Health Policy, Springer, vol. 14(4), pages 479-491, August.
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    20. Han-I. Wang & Alexandra Smith & Eline Aas & Eve Roman & Simon Crouch & Cathy Burton & Russell Patmore, 2017. "Treatment cost and life expectancy of diffuse large B-cell lymphoma (DLBCL): a discrete event simulation model on a UK population-based observational cohort," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(2), pages 255-267, March.
    21. Paul Jülicher & Vladimir P Chulanov & Nikolay N Pimenov & Ekaterina Chirkova & Anna Yankina & Claudio Galli, 2019. "Streamlining the screening cascade for active Hepatitis C in Russia: A cost-effectiveness analysis," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-22, July.
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