Journal
ANNALS OF APPLIED STATISTICS
Volume 3, Issue 1, Pages 458-488Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/08-AOAS206
Keywords
Functional principal component analysis (FPCA); multilevel models
Categories
Funding
- National Institute of Neurological Disorders and Stroke [R01NS060910]
- National Heart, Lung, and Blood Institute [HL083640, HL07578, AG025553]
- National Institute of Biomedical Imaging and BioEngineering [K25EB003491]
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The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The Volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.
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