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

BMC Public Health

Review

Eric C Sayre¹  Michael C Wolfson²  David L Buckeridge³  Michal Abrahamowicz³  Anya Okhmatovskaia³  Samuel Harper³  Douglas G Manuel⁴  Jillian Oderkirk⁵  William M Flanagan⁵  Philippe Finès⁵  M Mushfiqur Rahman⁶  Behnam Sharif⁶  Jacek A Kopec⁶ 

[1] Arthritis Research Centre of Canada, Vancouver, BC, Canada;Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, ON, Canada;Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada;Epidemiology Division, Ottawa Health Research Institute, University of Ottawa, Ottawa, ON, Canada;Health Analysis Division, Statistics Canada, Ottawa, ON, Canada;School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada;Arthritis Research Centre of Canada, Vancouver, BC, Canada;

关键词: Model Validation;    Microsimulation Model;    Citro;    Infectious Disease Model;    Alternative Model Structure;   

DOI  :  10.1186/1471-2458-10-710

 received in 2010-07-05, accepted in 2010-11-18,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundComputer 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.MethodsWe 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.ResultsEvidence 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.ConclusionAs 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.

【 授权许可】

CC BY   
© Kopec et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.