Simulation Modeling

In the context of CISNET, a model is a mathematical or computer-based model capable of approximating the complex system of cancer within a population over time. Such models can be analytic in nature where the system is represented by a series of mathematical closed-form equations. They can also use computer code to represent relationships and approximate the behavior of a system. Simulation models may have analytic subcomponents such as to describe cancer growth.

CISNET models are able to predict cancer outcomes like incidence and mortality in a population over time. To do so, most CISNET models use microsimulation modeling methods. With this method, a large number of persons are simulated and followed throughout their individual lifetimes. A simulated person’s life history may include events such as year of birth, accumulation of risk factors for cancer, age at development of preclinical cancer, ages at cancer progression and metastatic spread, diagnosis of cancer (through screening or symptomatic presentation), treatment of cancer, and death from cancer or other causes. By tallying these events and outcomes across simulated people, the models can gain insight into underlying dynamics of risk factors and cancer interventions in both historical populations and hypothetical scenarios.