University of Oxford
Analysis and management of large cohort real-world data for dementia-focused research
- Psychometric Analyst for the European Prevention of Alzheimer's Disease (EPAD) study.
- Senior Researcher and Data Manager for Dementias Platform UK (DPUK) and Real World Outcomes across the Alzheimer's disease spectrum for better care (ROADMAP).
Multi-disciplinary research and data management
Sarah is a senior researcher working across multiple projects. Sarah is programme lead for the DPUK datathon intiative, lead for the DPUK user group, programme lead for the DPUK data curation project and scientific lead for an international DPUK collaborative project in Alzheimer's disease. She is scientific reviewer for DPUK Data Portal research applications.
Sarah's psychometric work includes conducting Item Response Theory (IRT) analyses on mental health, lifestyle and healthcare scale data, investigating item-level information and scale reliability. She also utilises Structural Equation Modelling (SEM) to explore comorbidities, such as cognition and mental health, child adversity and adult biomedical outcomes and dementia. Her previous research focused on the cognitive predictors of falls and frailty in older adults, and poor mental health as a comorbidity with cognitive decline and dementia.
A psychometric evaluation of the 12-item EPQ-R neuroticism scale in 502,591 UK Biobank participants using item response theory (IRT)
Bauermeister S. and Gallacher J., (2020)
Real-world evidence in Alzheimer's disease: The ROADMAP Data Cube
Olin Janssen et al, (2019), Alzheimer's & Dementia
Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study
Chinmoy Sarkar et al, (2019), The Lancet Planetary Health, 3, e478 - e490
Environmental correlates of chronic obstructive pulmonary disease (COPD): A cross-sectional observational study of 96 779 participants from the UK Biobank
Webster C. et al, (2019), The Lancet Planetary Health
Assessing psychiatric comorbid disorders of cognition: A machine learning approach using UK Biobank data
Li C. et al, (2019)