| Affiliations: | DeBakey Research Leadership Program |
| Project Leader: | Irina Gaynanova irinag@tamu.edu Statistics |
| Faculty Mentor: | Dr. Irina Gaynanova (myself), Ph.D. |
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Meeting Times:
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Thu 4-5pm CST |
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Team Size:
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6
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| Open Spots: | 0 |
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Special Opportunities:
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– earn 491 credit; – flexible schedule besides required meeting time; – completely remote work; – opportunity to learn R and R package development; -potential of co-authorship on developed software products and resulting publications
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Team Needs:
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Proficient knowledge of R or python (a must; a plus if a student knows both languages), experience with git and GitHub (not necessary but a huge plus), proficiency with ggplot2 in R (not necessary but a huge plus), willingness to read literature on diabetes and CGMs, willingness to learn on the spot, willingness to work as part of the team, no conflict with team meeting times (Thursday 4-5pm).
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Description:
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Continuous glucose monitors (CGM) are small wearable devices that allow to measure the glucose levels continuously throughout the day, with some monitors taking measurements as often as every 5 minutes. In addition to providing patients with diabetes with frequent alerts on the status of their glucose levels, CGMs can supply researchers and clinicians with a wealth of data that has the potential to improve the diabetes management. However, while CGMs measure the time-dynamic of glucose profile and play an increasing role in clinical practice, their measurements are highly dependent on environmental and behavioral factors (i.e., meals, physical activity) that are often unknown. As a result, these data are often analyzed using very crude statistical measures (such as mean) that fail to fully explain clinical measures such as Hemoglobin A1C. The purpose of this project is to utilize publicly available as well as private CGM data for the purpose of comparing different statistical metrics and their associations with clinical outcomes, as well as developing new metrics based on the identified needs from the literature. The project is very flexible and the specific directions will be driven by the team developments. This is an early-stage project that has potential to expand in scope over several semesters based on interests of participants. Some specific tasks that students will be expected to do as part of the project: coding and testing existing code in R (possibly python), data processing (cleaning, reformatting), data visualization (in R primarily), literature review, statistical analyses of CGM data, development of new algorithms for processing/analyses
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