MCDTR: Methods and Measurement: Disease Modeling Software for Clinical Research

Michigan Model for Diabetes

To download the User Manual for Version 2.0 please click here.

The Michigan Model for Diabetes (MMD) is a computerized disease model that enables the user to simulate the progression over time of diabetes, its complications (retinopathy, neuropathy and nephropathy), and its major comorbidities (cardiovascular and cerebrovascular disease), and death. Transition probabilities can be a function of individual characteristics, current disease states or treatment status. The model also estimates the medical costs of diabetes and its comorbidities, as well as the quality of life related to the current health state of the subject.

For each subject, the model software reads in or simulates the subject’s baseline characteristics and then advances the subject through a specific number of years or until death. Each year, the model updates in the four stages as indicated by blue blocks in the following graph:

In contrast to other proposed models, the transition probabilities implemented in the MMD were obtained by synthesizing the published literature. Specifically, transition probabilities in the newly updated coronary heart disease model that reflect the direct effects of medical therapies on outcomes were derived from the literature and calibrated to recently published population-based epidemiologic studies and randomized controlled clinical trials. This method not only allowed us to build a model without access to individual-level data from a long-term prospective study, but allowed us to update the model by incorporating data from new studies as they become available.

In addition, different from other proposed models, our model allows the user to control risk factor changes through defining treatment threshold and compliance rates for dysglycemia, dyslipidemia, and hypertension, and compliance to quitting smoking and taking aspirin. Given the fact that modern medicines have largely decreased the complication rate in type 2 diabetes through management of these risk factors, it is important to explicitly model these management strategies and allow the user to modify them to match the specific scenario that they are simulating. For example, MMD explicitly models the treatment regimen for hyperglycemia through five treatment stages. The following figure shows a mock trajectory of A1c over time under this treatment regimen using 7% as the treatment threshold.

The following two figures show how A1c and SBP change over time in a simulated population.

The updated coronary sub-model has been internally and externally validated. Panel A shows the results of internal validation. Panel B shows the results of external validation. The MMD group continues to extend the external validation against the latest clinical and epidemiological data.

MMD is implemented using a general chronic disease modeling software IEST (Indirect Estimation and Simulation Tool) which provides an environment for model design, estimation, and simulation, as well as a convenient graphical user interface to (1) define parameters, (2) define populations, (3) generate populations from distributions, (4) create a new disease model or modify an existing model, (5) simulate the behavior of a given base population by using a defined model enhanced by a set of simulation rules, and (6) analyses and report simulation results. The software is being released for use by researchers under a general public license.