Health Outcomes and Biomedical Informatics 2019 Projects

Project Title: The Cost-Effectiveness of DPP-4 Inhibitors in Type 2 Diabetes Management in the Medicare Population

Faculty Mentor’s Name: Jinhai (Stephen)Huo

Student’s Name: Wade Chen

Project Description:

The goal of this project is to be able to characterize DPP-4 inhibitor use in the Medicare population, as well as quantify its overall cost-effectiveness as a type 2 diabetes treatment.

The primary data source will be the SEER-Medicare data set. Cohorts of patients with type 2 diabetes will be selected based on characteristics like gender, age, and disease state. The data will be managed in the data management software SAS. The data will be cleaned of any extraneous points. The cohort data will then be inputted into a Markov framework, which will model the diabetes disease state as a simultaneous progression of nephropathy, neuropathy, retinopathy, coronary heart disease (CHD), and stroke; all of these conditions lead to death. These cohorts will be tracked until their progression reaches death or 95 years old.

The cost-effectiveness ratio is calculated by taking the ratio between the difference in cost between and the difference in outcome (measured in QALYs) between the two Markov models. This ratio can be compared to the $50,000 threshold or other diabetes treatments with cost-effectiveness studies performed to determine relative efficacy. Further sensitivity studies can be performed by using different discount rates, removing certain assumptions, or manipulating other cost/utility variables. This can test the robustness of this model, and may also highlight key relationships that exist between certain variables.

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