Multilevel Matrix-Variate Analysis and its Application to Accelerometry-Measured Physical Activity in Clinical Populations. Issue 526 (3rd April 2019)

• Record Type: Journal Article
• Authors: Huang, Lei; Bai, Jiawei; Ivanescu, Andrada; Harris, Tamara; Maurer, Mathew; Green, Philip; Zipunnikov, Vadim
• Is Part Of: Journal of the American Statistical Association. Volume 114:Issue 526(2019)
• Page Start: 553
• Page End: 564
• Publication Date: 2019-04-03
• Subjects: Actigraphy -- Principal component analysis -- Separable covariance; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2018.1482750
• Languages: English
• ISSNs: 0162-1459

Abstract

ABSTRACT: The number of studies where the primary measurement is a matrix is exploding. In response to this, we propose a statistical framework for modeling populations of repeatedly observed matrix-variate measurements. The 2D structure is handled via a matrix-variate distribution with decomposable row/column-specific covariance matrices and a linear mixed effect framework is used to model the multilevel design. The proposed framework flexibly expands to accommodate many common crossed and nested designs and introduces two important concepts: the between-subject distance and intraclass correlation coefficient, both defined for matrix-variate data. The computational feasibility and performance of the approach is shown in extensive simulation studies. The method is motivated by and applied to a study that monitored physical activity of individuals diagnosed with congestive heart failure (CHF) over a 4- to 9-month period. The long-term patterns of physical activity are studied and compared in two CHF subgroups: with and without adverse clinical events. Supplementary materials for this article, that include de-identified accelerometry and clinical data, are available online.