Validation of population-based disease simulation models: a review of concepts and methods

被引:77
作者
Kopec, Jacek A. [1 ,2 ]
Fines, Philippe [3 ]
Manuel, Douglas G. [4 ]
Buckeridge, David L. [5 ]
Flanagan, William M. [3 ]
Oderkirk, Jillian [3 ]
Abrahamowicz, Michal [5 ]
Harper, Samuel [5 ]
Sharif, Behnam [1 ,2 ]
Okhmatovskaia, Anya [5 ]
Sayre, Eric C. [2 ]
Rahman, M. Mushfiqur [1 ,2 ]
Wolfson, Michael C. [6 ]
机构
[1] Univ British Columbia, Sch Populat & Publ Hlth, Vancouver, BC V5Z 1M9, Canada
[2] Arthritis Res Ctr Canada, Vancouver, BC, Canada
[3] STAT Canada, Hlth Anal Div, Ottawa, ON, Canada
[4] Univ Ottawa, Div Epidemiol, Ottawa Hlth Res Inst, Ottawa, ON, Canada
[5] McGill Univ, Dept Epidemiol Biostat & Occupat Hlth, Montreal, PQ, Canada
[6] Univ Ottawa, Dept Epidemiol & Community Med, Ottawa, ON, Canada
基金
加拿大健康研究院;
关键词
CANCER; HEALTH; UNCERTAINTY; MORTALITY; OSTEOARTHRITIS; ARCHIMEDES; FRAMEWORK; TOOL;
D O I
10.1186/1471-2458-10-710
中图分类号
R1 [预防医学、卫生学];
学科分类号
100235 [预防医学];
摘要
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.
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页数:13
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