Understanding all aspects of diabetes treatment is hindered by the complexity of this chronic disease and its multifaceted complications and comorbidities, including social and financial impacts. In vivo studies as well as clinical trials provided invaluable information for unraveling not only metabolic processes but also risk estimations of, for example, complications. These approaches are often time- and cost-consuming and have frequently been supported by simulation models. Simulation models provide the opportunity to investigate diabetes treatment from additional viewpoints and with alternative objectives. This review presents selected models focusing either on metabolic processes or risk estimations and financial outcomes to provide a basic insight into this complex subject. It also discusses opportunities and challenges of modeling diabetes.

https://journals.sagepub.com/doi/pdf/10.1177/1932296818785642

Modeling of Diabetes and Its Clinical Impact.

The Reference Model for Disease Progression is a league of models that compete amongst themselves. High Performance Computing (HPC) drives the Monte Carlo (MC) micro-simulation which generates large information volumes.The large amounts of information, model multiplicity, inputs from different sources, and human manipulation increases the chance for errors in the process.Therefore quality control measures are required. The model itself is a method of quality control for other modelers and already demonstrated how current disease models are becoming outdated. This paper will describe potential hazards and methods employed to increase model credibility.

The reference model for disease progression: data quality control

The Reference Model for disease progression is a league of models attempting to explain published study data. The model cross references simulation results with observed clinical results for multiple population cohorts. The model has grown to contain 47 different cohorts from 9 diabetic populations. Each population contains -40 rules and ~45 objectives. Since there are many repetitions and replications in the code, it becomes hard to maintain and expand further. To support future expansion, code reusability is needed. This is accomplished by an object oriented approach that allows inheriting population generation building blocks. The presentation will discuss the reorganization of population generation using the MIcro Simulation Tool - MIST. Examples demonstrating the benefit from the transformation towards the object oriented approach are presented. Results demonstrate model stability after code transformation.

The reference model uses object oriented population generation

The progression of a disease may be affected by many risk factors, such as gender, age, and current disease state. Such information is collected and made publically available by published clinical studies, yet combining this information into a disease model remains a challenge. This paper extends the previously published maximum likelihood estimation technique to estimate model parameters from indirect secondary data. Such information is available in the scientific literature so the modeler can access more data when estimating model parameters. The extension to the estimation procedure allows model transitions that depend on different sets of covariates for which secondary data are available. This extension uses a Markov model with transition probabilities stored in multi-dimensional tables accessed by covariate values. The paper uses a set of cases, including a case of cardiovascular disease in diabetes. The cases demonstrate the proposed method with various model variations. To help cope with model multiplicity, a selection method is demonstrated for picking a preferred model according to likelihood and structure criteria.

Estimation of a disease model based on a discrete time Markov model using secondary data with transitions based on multi-dimensional tables

This disclosure describes frameworks and techniques directed to the analysis and verification of models extracted from a database. In some cases, the database can include an online database, such as clinicaltrials.gov administered by the United States National Institutes of Health. In particular, this disclosure describes implementations that utilize models derived from clinical trial data extracted from a database and analyzes the models. The analysis of the models can be used to verify the results of the clinical trials from which the models were derived. Additionally, the analysis of the models can identify a combination of models that can be used to predict health outcomes of one or more biological conditions for one or more populations.

Analysis and verification of models derived from clinical trials data extracted from a database

A method wherein reference disease models predict progression of disease within given populations, utilizing publically available clinical data and risk equations, to give a birds-eye view of clinical trials by allowing multiple trials to be systematically compared simultaneously via parallel processing/High Performance Computing which allows competition among alternative equations/hypothesis combinations; cross validation; and, then ranks results according to fitness via a fitness engine.

http://conference.scipy.org/proceedings/scipy2012/pdfs/jacob_barhak.pdf

Jacob Barhak

Papers

Reference model for disease progression