Leveraging DICE (Discretely-Integrated Condition Event) Simulation to Simplify the Design and Implementation of Hybrid Models

Objectives: Using an example of an existing model constructed by the National Institute for Health and Care Excellence (NICE) to inform a real health technology assessment, this study seeks to demonstrate how a discretely integrated condition event (DICE) simulation can improve the implementation of Markov models. Methods: Using the technical report and spreadsheet, the original model was translated to a standard DICE simulation without making any changes to the design. All original analyses were repeated and the results were compared. Aspects that could have improved the original design were then considered. Results: The original model consisted of 32 copies (8 risk strata x 4 treatments) of the Markov structure, containing more than 6000 Microsoft Excel (R) formulas (18 MB files). Three aspects (nonadherence, scheduled treatment stop, and end of fracture risk) were handled by incorporating weighted averages into the cycle-specific calculations. The DICE implementation used 3 conditions to represent the states and a single transition event to apply the probabilities; 3 additional events processed the special aspects, and profiles handled the 8 strata (0.12 MB file). One replication took 16 seconds. The original results were reproduced but extensive additional sensitivity analyses, including structural analyses, were enabled. Conclusion: Implementing a real Markov model using DICE simulation both preserves the advantages of the approach and expands the available tools, improving transparency and ease of use and review.