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Unpublished Paper
LONGITUDINAL HIGH-DIMENSIONAL DATA ANALYSIS
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Sonja Greven, Department of Statistics, Ludwig-Maximilians-Universitat and Munchen
  • Brian Caffo, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Daniel S. Reich, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health
  • Ciprian Crainiceanu, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Date of this Version
11-16-2011
Abstract

We develop a flexible framework for modeling high-dimensional functional and imaging data observed longitudinally. The approach decomposes the observed variability of high-dimensional observations measured at multiple visits into three additive components: a subject-specific functional random intercept that quantifies the cross-sectional variability, a subject-specific functional slope that quantifies the dynamic irreversible deformation over multiple visits, and a subject-visit specific functional deviation that quantifies exchangeable or reversible visit-to-visit changes. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels.

Citation Information
Vadim Zipunnikov, Sonja Greven, Brian Caffo, Daniel S. Reich, et al.. "LONGITUDINAL HIGH-DIMENSIONAL DATA ANALYSIS" (2011)
Available at: http://works.bepress.com/ciprian_crainiceanu/31/