DICE Modelling Platform
DICE is a flexible simulation tool used to model how diseases and treatments affect patients over time. It can track multiple health conditions at once, simulate individual patient experiences, and calculate costs and quality of life. It’s designed to be transparent and easy to use, especially in Excel.
At a glance
Use when
Modeling complex diseases with multiple interacting conditions, when transparency and auditability are critical, or when moving beyond cycle-based Markov assumptions
Avoid when
Resource constraints or queuing dynamics are central to the analysis, or when high-performance computing environments are required for large simulations
Inputs
Patient-level predictors, condition levels, event probabilities, transition rules, valuation functions (e.g., cost, utility), time horizons
Outputs
Simulated disease trajectories, cost and outcome profiles (e.g., total cost, QALYs), individual and aggregate results, probabilistic sensitivity analyses
How it works
Discretely Integrated Condition Event (DICE) Simulation is a unified modeling framework for pharmacoeconomic analysis. It integrates conditions (persistent states with variable levels) and events (instantaneous occurrences) through discrete time updates. Supports cohort and microsimulation, deterministic or stochastic execution, and concurrent valuation of conditions and events (e.g., cost, utility). Implemented in structured tables, often in MS Excel. Subsumes Markov models and non-resource-constrained discrete event simulations within one formulation.
- Project
- IMPACT HTA
- Funding
- Horizon 2020
- Project status
- Completed 2021
- HTA domains
- Costs & Economic Evaluation
- Categories
- Cost-effectiveness Modelling
- Technology
- Non-specific
- Assumptions
- Conditions evolve based on discrete integration with events; event timing and effects are defined by model logic; patient heterogeneity is captured via determinant profiles; all changes occur at discrete time points aligned with event occurrences
- Strengths
- High transparency and auditability, especially in Excel; avoids artificial constraints like fixed cycles in Markov models; supports complex, individualized patient pathways; integrates multiple valuations in one run; flexible execution modes (cohort/microsimulation, deterministic/stochastic)
- Limitations
- May require advanced Excel skills or custom scripting for complex models; performance limitations with large-scale microsimulations in Excel; less support for resource constraints compared to traditional discrete event simulation tools
- Also known as
- DICE Simulation, Discretely Integrated Condition Event Simulation
Questions this answers
- › How can I model multiple coexisting health conditions in a single patient over time?
- › What is a transparent, Excel-based method for health economic modeling?
- › How do I avoid the 'one-transition-per-cycle' limitation of Markov models?
- › Can I run both cohort and individual-level simulations in the same framework?
- › How can I apply multiple outcomes like cost, utility, and willingness-to-pay simultaneously?
- › What modeling approach supports both deterministic and stochastic simulations?
References & sources
Similar by meaning
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.