Discrete event simulation: the preferred technique for health economic evaluations?

Area of Science:

• Health economics
• Computational modeling
• Decision science

Background:

• Health economic evaluations are crucial for informed decision-making.
• Existing data limitations necessitate robust modeling techniques.
• Oversimplified models can yield inaccurate and misleading results.

Purpose of the Study:

• To advocate for discrete event simulation (DES) over cohort Markov models (CMMs).
• To highlight the limitations of CMMs in health economic evaluations.
• To establish DES as the preferred modeling technique for healthcare decision-making.

Main Methods:

• Review of underlying principles for both modeling techniques.
• Assessment of model suitability based on data availability and decision-making needs.
• Comparison of DES and CMMs regarding accuracy, relevance, and computational feasibility.

Main Results:

• Cohort Markov models possess significant limitations and assumptions, rendering them inadequate for most healthcare decisions.
• Discrete event simulation (DES) is better suited for modeling complex health-related scenarios.
• DES provides more accurate and relevant estimates without prohibitive computational costs.

Conclusions:

• Discrete event simulation (DES) is the preferred method for current health economic evaluations.
• DES offers a more transparent and valid approach to modeling healthcare consequences.
• The advantages of DES outweigh the challenges of data requirements and transparency.