Metamodel of a Simulation Model of Colorectal Cancer with Diverse Clinic Populations and Intervention Scenarios

Colorectal cancer (CRC) prevention is dependent on increasing screening rates, a strategy proven effective in reducing cancer cases and potential life years lost. Simulation models of CRC can be used to project expected outcomes associated with different evidence-based interventions. However, traditional simulation for each population of interest is computationally intensive and requires a model expert. To address this, we proposed a metamodeling approach, considering various techniques such as linear regression and random forest. By creating a metamodel of the simulation, decision makers can generate both individual and population-level estimates directly and instantaneously. We aimed to create a metamodel of an existing CRC simulation model that can be adapted for different interventions and populations to predict cancer cases averted and life years lost.