Uncertainty Analysis in Population-Based Disease Microsimulation Models

Abstract EN

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.