Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling

Area of Science:

• Health Economics
• Computational Biology
• Medical Informatics

Background:

• Precision medicine (PM) interventions present complex decision-making and patient heterogeneity.
• Traditional economic evaluation methods like Markov models have limitations in capturing PM complexities.
• There is a growing need for robust methods to assess the economic value of PM.

Purpose of the Study:

• To describe the unique challenges in economic evaluations of precision medicine interventions.
• To present potential solutions and approaches using simulation modeling methods.
• To highlight the suitability of simulation models for PM economic evaluations.

Main Methods:

• Utilizing dynamic simulation models, including discrete event simulation and agent-based models.
• Developing mathematical representations of complex healthcare systems and intervention scenarios.
• Employing patient-level simulation models to address heterogeneity and patient-specific pathways.

Main Results:

• Dynamic simulation models can address methodological challenges in modeling PM.
• These models capture patient-specific pathways, companion diagnostics, and test sequencing.
• Patient heterogeneity can be effectively managed using patient-level simulation.

Conclusions:

• Economic evaluation of PM interventions faces unique methodological hurdles.
• Simulation models are well-suited for PM economic evaluations due to patient-level analysis capabilities.
• Simulation models can capture intervention dynamics within complex healthcare delivery systems.