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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Kronecker product linear exponent AR(1) correlation structures for multivariate repeated measures.

Sean L Simpson1, Lloyd J Edwards2, Martin A Styner3

  • 1Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America ; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

Plos One
|March 4, 2014
PubMed
Summary
This summary is machine-generated.

A new statistical model, the Kronecker product linear exponent autoregressive (LEAR) correlation structure, enhances longitudinal medical imaging analysis. This flexible model accurately captures complex spatio-temporal patterns in biological data.

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Area of Science:

  • Statistics
  • Medical Imaging
  • Neuroscience

Background:

  • Longitudinal imaging studies are crucial for understanding biological changes over time.
  • Modeling correlations in such data requires flexible yet parsimonious structures.
  • Existing models like continuous-time AR(1) may lack the necessary adaptability.

Purpose of the Study:

  • To introduce the Kronecker product linear exponent autoregressive (LEAR) correlation structure.
  • To provide a flexible and parsimonious model for multivariate repeated measures data.
  • To demonstrate its utility in analyzing longitudinal medical imaging data.

Main Methods:

  • Proposed the Kronecker product LEAR correlation structure, extending the one-dimensional LEAR model.
  • The model incorporates two-factor dependencies (e.g., spatial and temporal).
  • Evaluated analytic and numerical properties for computational efficiency.

Main Results:

  • The Kronecker product LEAR structure offers a balance between model flexibility and parameter parsimony.
  • It effectively models correlations induced by multiple factors in repeated measures.
  • Demonstrated application using longitudinal data of caudate morphology in schizophrenia.

Conclusions:

  • The Kronecker product LEAR model is a valuable addition to statistical methods for longitudinal data analysis.
  • It provides a robust framework for characterizing complex spatio-temporal patterns in medical imaging.
  • The model's properties make it suitable for diverse applications in biomedical research.