Model Transparency and Validation

David M. Eddy, William Hollingworth, J. Jaime Caro, Joel Tsevat, Kathryn M. McDonald and John B. Wong

Medical Decision Making, 2012, vol. 32, issue 5, 733-743

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.

Keywords: modeling; transparency; validation; good practices; simulation; decision sciences (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:32:y:2012:i:5:p:733-743

DOI: 10.1177/0272989X12454579

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