Discretely Integrated Condition Event (DICE) Simulation for Pharmacoeconomics.

Area of Science:

• Health economics
• Computational modeling
• Pharmacoeconomics

Background:

• Decision-analytic modeling is crucial for pharmacoeconomic analyses.
• Existing methods often require simplifying assumptions or introduce complexity.
• A need exists for a more unified and transparent modeling approach.

Purpose of the Study:

• To introduce Discretely Integrated Condition Event (DICE) simulation as a unifying pharmacoeconomic modeling technique.
• To present DICE as a transparent and flexible approach that avoids unnecessary assumptions.
• To demonstrate the capability of DICE to handle complex modeling requirements.

Main Methods:

• DICE simulation models conditions (persistent aspects with changing levels) and events (instantaneous occurrences modifying conditions or timing).
• Conditions and events are integrated by updating condition levels at event occurrences.
• Patient heterogeneity is accommodated through profiles of determinant values.
• Multiple valuations (utility, cost, willingness-to-pay) can be applied concurrently.
• Models are specified in tables and implementable in MS Excel for ease of review and validation.

Main Results:

• DICE simulation integrates state-transition (Markov) and discrete event simulation within a single framework.
• The approach accommodates cohort or microsimulation execution.
• Models can be run deterministically or stochastically.
• DICE offers a straightforward and transparent method for pharmacoeconomic modeling.

Conclusions:

• DICE simulation provides a flexible, transparent, and unifying framework for pharmacoeconomic decision-analytic modeling.
• It reduces the need for simplifying assumptions common in other methods.
• The MS Excel implementation facilitates accessibility and validation of complex health economic models.