Discrete Event Simulation for Decision Modelling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening

Abstract

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions, but they are sometimes not flexible enough to allow accurate modelling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000-£30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include the exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening versus those not invited. The model was validated against key events and cost-effectiveness as observed in a large randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies.