Microsimulation models for cost-effectiveness analysis: a review and introduction to CEAM

In health policy and planning, decision-makers often need to consider the expected costs and population health outcomes of alternative choices. To aid in this effort, we developed a generalized microsimulation framework for estimating the cost-effectiveness of health interventions. We began by evaluating 18 existing microsimulation frameworks, including options based on the R programming language (e.g. msm, MicSim), Python (e.g. Liam-2, MIST) and standalone software (e.g. TreeAge, AnyLogic). We developed "hello, world" models in several frameworks and assessed them on computation speed, ease of use, and other criteria. This led us to develop a new framework in Python: a discrete-time Markov model called Cost-Effectiveness Analysis with Microsimulation (CEAM). In this paper, we present results from the review and demonstrate how to build a simple simulation with CEAM.