Method: Combining the Archimedes model, response surface methodologies, and big data tools, we created a platform that makes evaluating a highly complex disease and healthcare model interactive, while not requiring statistical programming. The Archimedes Model is a large-scale simulation model of chronic disease and healthcare systems. In this method, an individual level dataset is generated with the simulation model and stored in high performance databases for rapid retrieval. In creating the dataset, the simulation model is run for a broad population of individuals, and each individual is simulated repeatedly for a set treatment scenarios specified by a design of experiments. At run time, the user specifies the study subpopulation in terms of clinical parameters. That subpopulation is extracted from the dataset, and response surfaces (e.g. meta-models) are generated. Subsequently, user-specified treatment scenarios can be estimated from the response surfaces, providing estimates equivalent to what the large-scale simulation model would forecast if it were evaluated directly.
Result: We generated an individual level dataset containing 100,000 US Adults with chronic obstructive pulmonary disease (COPD), forecasting biomarkers and outcomes over 5 years, in 50 treatment scenarios, exploring a set of 8 COPD related treatment parameters (such as improved FEV1, FVC, reduced risks of exacerbations, levels of existing treatments, and smoking cessation). The treatment scenarios were specified by a D-optimal design of experiments for continuous and categorical parameters. Using the platform, we can define the study cohort described in the UPLIFT Study of Tiotropium, model the Tiotropium intervention, and obtain estimates in-line with the published trial results in just minutes.
Conclusion: With this platform, large-scale simulation models can be used interactively and without statistical programming. This platform makes generating hypotheses faster, and accelerates the decision making process from months to minutes.