IEST is a general software application designed to model the progression of chronic diseases. The software allows modeling multiple disease processes in parallel based on a state transition model. The user defines states, transition probabilities, model variations, parameters, population data and many other details to specify the model and sample and then simulates the disease process for a number of years.
Here are highlights of current capabilities of the software:
- Series of states: The model is specified as series of states. Each series is a sequence (usually defined by severity); each state may be a stage in a chronic disease or an instantaneous event (e.g. myocardial Infarction and stroke).
- Multiple nested parallel series: Several disease processes (series) can be specified in parallel, such as complications or comorbidities. Such disease processes can also be nested within each other. This allows modeling multiple diseases and mortality from other causes in a conceptually easy manner. See the overall structure of Michigan Model for Diabetes as an example.
- Transition probabilities as functions: The transition from state to state is specified by a probability; the probability can be a constant or described by a mathematical function of individual characteristics, of medical history, or of other parameters such as intervention parameters. These functions can use general mathematical functions such as "Exp", or "Log", or conditional functions involving "If" statements.
- Monte-Carlo Simulation: The system can perform Monte-Carlo simulation of a model using a predefined population at baseline. The simulation can include pre-processing and post-processing stages that allow changing individual characteristics, intervention parameters, and cost parameters. The user can specify costs and health utilities for each stage of disease or event and the number of years to be modeled.
- Output: The current MMD software provide raw simulated data for all simulated individual, e.g. risk factors, complications status, year cost and utility score for each simulated year. The software also provides simple summaries on output, which can include the number of subjects who enter or pass through a stage of disease, the number who die and cause of death, the total cost and average health utility. The user can control the amount of output provided by the simulation.
- Limited Version control: The system supports the storage of multiple versions of a model and of different population sets. This allows running the same simulation over different combinations of models and baseline population sets. Files saved by the system are backed up with a time stamp to allow rollback to a previously saved version.
- Import and Export: Simulation results and population set data can be exported using Comma Separated Values (CSV) format. Population data can be imported using the same format. This allows manipulation of system data in applications such as a spreadsheet or a database.
- Graphic User Interface (GUI) and Report Generator: The system has a dedicated GUI composed of forms. It allows users to define and modify states, transitions, models, population data and other entities. It contains wizards to help define cost and quality of life parameters.
- Estimation of Model parameters: The Lemonade method previously offered as a Matlab prototype is now incorporated as Python code that can be used from within the system. The GUI makes it easier to design the estimation project and create a simulation project from the estimation code.
- High Performance Computing: The system can use a computer cluster to run simulations on many CPU cores in parallel, allowing more complex simulations in shorter time. Once results are reached they can be forwarded as tables and plots via email.
A set of examples is provided with the software to demonstrate its capabilities as well as a developer guide that explains the system from a technical point of view. Further information about the software can be found in its help system that is provided online for reference.