Discrete Event Simulation: A Flexible Framework for Cost-Effectiveness Analysis in Health Care (Motivated by the Evaluation of Gynaecological Cancer Surveillance Strategies for Women with Lynch Syndrome by Snowsill et al.)

Discrete event simulation (DES) offers a flexible and detailed approach to cost-effectiveness analysis in healthcare, particularly when patient pathways are heterogeneous and evolve over long time horizons. This report introduces DES as an individual-level modeling strategy using the evaluation of gynecological cancer surveillance strategies in women with Lynch syndrome as a case study. The simulation captures complex clinical events such as diagnosis, surveillance uptake, surgical interventions, and mortality by integrating diverse clinical, epidemiological, and economic data. The study by Snowsill et al. (2024) exemplifies how DES can model competing strategies, account for variations in risk profiles and timing, and assess outcomes through lifetime cost-utility analyses. Results are illustrated through tables and figures, including sensitivity analyses that show how utility scores impact cost estimates. DES is shown to improve model realism and decision-making transparency, while also highlighting challenges like data demands and model complexity. Future applications should focus on real-world data integration, methodological standardization, and scalability. DES stands out as a valuable tool for policy-relevant health economic evaluations in contexts where traditional models fall short.