Table of Contents
Epidemiology Research International
Volume 2012, Article ID 610405, 14 pages
Research Article

Uncertainty Analysis in Population-Based Disease Microsimulation Models

1School of Population and Public Health, University of British Columbia, 2329 West Mall, Vancouver, BC, Canada V6T 1Z4
2Arthritis Research Centre of Canada, 895 10th Avenue West, Vancouver, BC, Canada V5Z 1L7
3Methodology & Statistics, CIHR Canadian HIV Trials Network, St. Paul's Hospital, No. 620, 1081 Burrard Street, Vancouver, BC, Canada V6Z 1Y6
4Health Analysis Division, Statistics Canada, 150 Tunney's Pasture Driveway, Ottawa, ON, Canada K1A 0T6
5Faculty of Medicine, University of Ottawa, 550 Cumberland Street Ottawa, ON, Canada K1N 6N5

Received 27 February 2012; Revised 9 June 2012; Accepted 12 June 2012

Academic Editor: Carolyn Rutter

Copyright © 2012 Behnam Sharif et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models.