Infer the timing of menopause from electronic health record (EHR) longitudinal laboratory data
Marisa Medina, Professor
UC San Francisco
Open. Apprentices needed for the fall semester. Enter your application online beginning August 21st. The deadline to apply is Monday, August 31st, 4pm.
Electronic health record (EHR)-linked biobanks, such as the All of Us Research Program, do not contain reliable information about the timing of menopause for most women. Recent studies suggest that physiological changes associated with menopause can be detected through longitudinal patterns in routinely measured clinical biomarkers (e.g., in metabolic panels or complete blood counts), providing an opportunity to more accurately infer menopause timing from EHR laboratory data.
Role: 1) The undergraduate would learn about human subjects research and be trained to protect human subjects before analyzing human subjects data.
2) Under direction of the research supervisor, the undergraduate would curate and analyze data from the All of Us Research Program and other sources, generating descriptive statistics and performing quality control to arrive at a final dataset for analysis.
3) The undergraduate would learn and/or expand their knowledge of statistical analyses to identify inflection points in longitudinal data.
4) Under direction of the research supervisor, the undergraduate would statistically compute menopause timing estimates for individual women from multiple data sources.
Qualifications: An interest and some basic experience with statistics and coding would be a good foundation for this project.
A motivated student with a desire to analyze datasets that are relevant to human (women's) health would be ideal.
It would be desirable but not essential for the student to have prior experience with longitudinal data analyses and/or have proficiency in at least one programming language.
Day-to-day supervisor for this project: Beth Theusch, Staff Researcher
Hours: to be negotiated
Off-Campus Research Site: This is a data analysis project, so project can be conducted completely remotely. The Medina lab is physically located at 5700 Martin Luther King Jr Way in Oakland, but the research supervisor (Beth Theusch) works remotely.
Related website: https://profiles.ucsf.edu/elizabeth.theusch
Related website: https://medinalab.ucsf.edu/