A Tutorial on Discrete Event Simulation Models in R Using a Cost-Effectiveness Analysis Example

Discrete Event Simulation (DES) is a flexible and computationally efficient approach for modeling diverse processes; however, DES remains underutilized in healthcare and medical decision-making due to a lack of reliable and reproducible implementations. We developed an open-source DES framework in R to simulate individual-level state-transition models (iSTMs) in continuous time accounting for treatment effects, time dependence on state residence, and age-dependent mortality.Our DES implementation employs a modular and easily adaptable structure, with each module corresponding to a unique transition between health states. To simulate the evolution of the process (i.e., individual state transitions), we adapted the next-reaction algorithm from the stochastic chemical reactions literature. Simulation-time dependence (age-dependent mortality) and state residence time dependence (transition from Sick to Sicker) are seamlessly incorporated into the DES framework via validated non-parametric and parametric sampling routines (e.g., inversion method) of event times. Treatment effects are integrated as scaling factors of the hazard functions (proportional hazards). We illustrate the framework’s benefits by implementing the Sick-Sicker Model and conduct a cost-effectiveness analysis and probabilistic sensitivity analysis. We also obtain epidemiological outcomes of interest from the DES output, such as disease prevalence, survival probabilities, and distributions of state-specific dwell times. Our DES framework offers a reliable and accessible alternative that enables the simulation of more realistic dynamics of state-transition processes at potentially lower implementation and computational costs than discrete time iSTMs.