Validation of population-based disease simulation models: a review of concepts and methods.
Background: Computer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.Methods: We developed...
| Publicado en: | BMC Public Health Vol. 10; no. 1; pp. 710 - 711 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | review Journal Article |
| Publicado: |
BioMed Central
2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104993968&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104993968 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712458 1CIK jtl: BMC Public Health issn: 14712458 maglogo: N pubinfo: dt: 2010 vid: 10 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 104993968 NLM21087466 2010916463 10.1186/1471-2458-10-710 NLM21087466 PMC3001435 104993968 ppf: 710 ppct: 1 formats: tig: atl: Validation of population-based disease simulation models: a review of concepts and methods. aug: au: Kopec, Jacek A Finès, Philippe Manuel, Douglas G Buckeridge, David L Flanagan, William M Oderkirk, Jillian Abrahamowicz, Michal Harper, Samuel Sharif, Behnam Okhmatovskaia, Anya Sayre, Eric C Rahman, M Mushfiqur Wolfson, Michael C affil: School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. jkopec@arthritisresearch.ca sug: subj: Chronic Disease Epidemiology Computer Simulation Standards Models, Theoretical Validation Studies Public Health Funding Source ab: Background: Computer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.Methods: We developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.Results: Evidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.Conclusion: As the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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