The University of Chicago's Women's Brain Health research program is seeking to collect, organize, and analyze information from internal and external data systems to support projects focused on modifiable factors influencing Alzheimer's disease risk, with a particular emphasis on sex-specific (female) risk and identifying risk/resilience factors during menopause.
Requirements
- Foundational knowledge and hands-on practice in core statistical methods – descriptive inference, probability, linear/logistic regression – with implementation in R/Python and clear interpretation.
- Hands-on experience harmonizing cognitive assessment data and applying measurement invariance/IRT/score linking.
- Practical knowledge of missing data (MICE, weighting).
- Experience publishing harmonized datasets and reproducible reports (R Markdown/Quarto/Jupyter).
- Foundational knowledge and hands-on practice in survival analysis, mixed-effects models and longitudinal modeling.
- Experience with health data standards (ICD, SNOMED, LOINC, HL7 FHIR or OMOP) and unit/scale conversions (UCUM).
- Programming and Coding experience.
Responsibilities
- Implement the research analyses, executing large-scale data harmonization, statistical analysis, and modeling – including predictive models for Alzheimer’s disease with a particular focus on sex-specific (female) risk.
- Acquiring, cleaning, and organizing datasets; mapping and assessing available cohorts and sources by profiling variable coverage, coding systems, and cognitive instruments; prototyping an end-to-end harmonization on subsets of several cohorts with documented variable/value maps and quality-control checks; validating measurement invariance and IRT linking on one to two cognitive scales to produce crosswalks and uncertainty summaries; and delivering an analysis-ready, versioned dataset accompanied by a data dictionary and a concise user guide.
- Leads the acquisition, cleaning, and harmonization of secondary datasets from the multiple sources, including international cohort studies, with support from Dr. Farina and the project team.
- Conducts data exploration and statistical analyses to extract meaningful insights from large, complex datasets, with support from Dr. Farina, Dr. Capuano and the project team.
- Unify different types of data including cognitive instruments (e.g., MMSE, MoCA, Trails, Digit Symbol, HVLT, etc.): perform measurement invariance testing; build IRT/linking models and score crosswalks; document comparability limits.
- Correct site/batch effects and temporal drift using mixed-effects models, empirical Bayes approaches, and sensitivity analyses.
- Maintains and analyzes statistical models using best practices in machine learning, statistical inference, and reproducible research workflows.
Other
- Minimum requirements include a college or university degree in related field.
- Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.
- Graduate college or university degree.
- Masters degree in Biostatistics, Statistics, Epidemiology, Psychometrics, Data Science, or related field.
- Excellent written and oral communication.
