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Abstract:
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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. This work is motivated by the Sleep Heart Health Study (SHHS), a comprehensive 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. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes. Though motivated by the SHHS, the proposed methodology is generally applicable to many modern scientific studies of hierarchical functional outcomes.
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