A New Method for Model-Based Health Economic Evaluation Utilizing and Extending Moment-Generating Functions

Area of Science:

• Health economics
• Mathematical modeling
• Biostatistics

Background:

• Health economic evaluations often use Markov models or discrete event simulation (DES).
• Markov models struggle with time-dependent events (sojourn time) without tunnel states.
• DES offers flexibility but Monte Carlo variation limits analysis complexity.

Purpose of the Study:

• Introduce a novel methodological framework for health economic modeling.
• Extend the concept of moment-generating functions (MGFs) for time-to-event variables.
• Improve efficiency and capability in calculating lifetime costs and health effects.

Main Methods:

• Utilize moment-generating functions (MGFs) for time-to-event variables.
• Efficiently calculate discounted life-years and costs in health states.
• Incorporate competing risks and polynomial time-dependent payoffs.

Main Results:

• The MGF method efficiently calculates discounted life-years and costs.
• It accommodates various payoff structures: constant, one-off, exponential, and polynomial.
• A worked example demonstrates practical application and compares performance against Markov and DES.

Conclusions:

• The MGF method is a viable alternative to Markov modeling and DES.
• It offers advantages in efficiency, accuracy, and handling complex scenarios.
• The method provides a powerful tool for health economic evaluations.