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This project aims at enlightening issues on health economics and precision medicine in diabetes treatment and prevention, with specific reference to reimbursement of new drugs. Notably, diabetes is involved with various risk factors for onset and complications, including genetics (family history), life style and BMI. Additionally, situational issues come in concerning comorbidities, polypharmacy and adherence to therapies. Known biomarkers concern blood sugar levels and Hb1Ac, but also nephropathic complication-related biomarkers such as albumin.
Associations of biomarkers, risk factors and situational issues with hard outcomes such as hospitalizations (that can be economically valued) will be investigated using secondary analysis on clinical trials and analysis of real-world data to identify patient subgroups most benefiting or being minimally harmed with targeted therapies.
The project is in the context of a PhD with building blocks being
To pave the way for cost-effectiveness analyses of personalized diabetes treatment
This project includes the essential steps for developing a new health economic diabetes model that is able to assess personalized medicine (1) describe the currently available models and their fitness to assess personalized medicine, (2) assess associations of real-life patient characteristics (non-adherence, comorbidities, biomarkers) with morbidity and mortality, (3) incorporate these personalized patient profiles and develop and validate a new health economic model
For chapters 1-2: literature reviews
For chapter 3-4: GIANTT real-world database analyses
For chapter 5-6: modelling using R and/or Excel
This project will give insight in the current status of the health economic assessments of personalized diabetes treatment, and provide an optimized model taking into account patient heterogeneity.
Important links can be made within the Drug Regulation Domain and the Drug Application Domain. This may lead to expanding the patient-level factors that will be assessed in reimbursement decision making; as well as support healthcare professionals in making personalized decisions.
This is a research project of PROMINENT