High Dimensional Data


Group leader

Teresa Filshtein
University of California, San Francisco, USA

Dementia research, as most health research fields, is facing new challenges and opportunities with growing sources of data and the emergence of high-dimensional data. These data include administrative databases, omics data, brain imaging data (MRI, PET), biomarkers panels assessing gene expression and metabolic pathways, among many others.
Challenges in the statistical analyses of high-dimensional data are numerous, even when the number of observations is much larger than typically available in research cohorts. Big data does not necessarily resolve the familiar internal and external validity challenges in epidemiological studies and may in some situations exacerbate challenges with measurement validity and selection bias.