Kronecker product linear exponent AR(1) correlation structures for multivariate repeated measures

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference...

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Bibliographic Details
Published in:PloS one 2014-02, Vol.9 (2), p.e88864-e88864
Main Authors: Simpson, Sean L, Edwards, Lloyd J, Styner, Martin A, Muller, Keith E
Format: Article
Language:English
Subjects:
Antipsychotics
Autoregressive processes
Biology
Caudate Nucleus - pathology
Correlation
Correlation analysis
Data analysis
Decay rate
Econometrics
Economic models
Humans
Image Processing, Computer-Assisted - methods
Life span
Mathematical functions
Mathematical models
Mathematical programming
Mathematics
Medical imaging
Medical imaging equipment
Medical research
Medical schools
Medicine
Mental disorders
Models, Theoretical
Morphology
Multivariate Analysis
Numerical analysis
Parameterization
Schizophrenia
Schizophrenia - pathology
Statistical methods
Temporal variations
Time-Lapse Imaging - methods
Within-subjects design
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Summary:Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0088864