Literature DB >> 22990088

Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7.

David M Eddy1, William Hollingworth2, J Jaime Caro3, Joel Tsevat4, Kathryn M McDonald5, John B Wong6.   

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.

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Year:  2012        PMID: 22990088     DOI: 10.1177/0272989X12454579

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  142 in total

1.  Reducing added sugars in the food supply through a cap-and-trade approach.

Authors:  Sanjay Basu; Kristina Lewis
Journal:  Am J Public Health       Date:  2014-11-03       Impact factor: 9.308

2.  Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.

Authors:  Christopher H Jackson; Mark Jit; Linda D Sharples; Daniela De Angelis
Journal:  Med Decis Making       Date:  2013-07-25       Impact factor: 2.583

3.  Myths and Misconceptions of Within-Cycle Correction: A Guide for Modelers and Decision Makers.

Authors:  Elamin H Elbasha; Jagpreet Chhatwal
Journal:  Pharmacoeconomics       Date:  2016-01       Impact factor: 4.981

Review 4.  A critical review of cost-effectiveness analyses of vaccinating males against human papillomavirus.

Authors:  Yiling Jiang; Aline Gauthier; Maarten J Postma; Laureen Ribassin-Majed; Nathalie Largeron; Xavier Bresse
Journal:  Hum Vaccin Immunother       Date:  2013-07-23       Impact factor: 3.452

5.  Patients with unilateral transfemoral amputation treated with a percutaneous osseointegrated prosthesis: a cost-effectiveness analysis.

Authors:  E Hansson; K Hagberg; M Cawson; T H Brodtkorb
Journal:  Bone Joint J       Date:  2018-04-01       Impact factor: 5.082

6.  Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Jeanne S Mandelblatt; Hui Huang; Mehmet Ali Ergun; Elizabeth S Burnside; Cong Xu; Yisheng Li; Oguzhan Alagoz; Sandra J Lee; Natasha K Stout; Juhee Song; Amy Trentham-Dietz; Sylvia K Plevritis; Sue M Moss; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

7.  Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.

Authors:  Oguzhan Alagoz; Donald A Berry; Harry J de Koning; Eric J Feuer; Sandra J Lee; Sylvia K Plevritis; Clyde B Schechter; Natasha K Stout; Amy Trentham-Dietz; Jeanne S Mandelblatt
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

8.  Cost-effectiveness of CT angiography and perfusion imaging for delayed cerebral ischemia and vasospasm in aneurysmal subarachnoid hemorrhage.

Authors:  P C Sanelli; A Pandya; A Z Segal; A Gupta; S Hurtado-Rua; J Ivanidze; K Kesavabhotla; D Mir; A I Mushlin; M G M Hunink
Journal:  AJNR Am J Neuroradiol       Date:  2014-05-08       Impact factor: 3.825

9.  A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.

Authors:  Fernando Alarid-Escudero; Eline M Krijkamp; Petros Pechlivanoglou; Hawre Jalal; Szu-Yu Zoe Kao; Alan Yang; Eva A Enns
Journal:  Pharmacoeconomics       Date:  2019-11       Impact factor: 4.981

10.  Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Eveline A Heijnsdijk; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

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