Open Access
March 2016 Applying a spatiotemporal model for longitudinal cardiac imaging data
Brandon George, Thomas Denney, Jr., Himanshu Gupta, Louis Dell’Italia, Inmaculada Aban
Ann. Appl. Stat. 10(1): 527-548 (March 2016). DOI: 10.1214/16-AOAS911


Longitudinal imaging studies have both spatial and temporal correlation among the multiple outcome measurements from a subject. Statistical methods of analysis must properly account for this autocorrelation. In this work we discuss how a linear model with a separable parametric correlation structure could be used to analyze data from such a study. The goal of this paper is to provide an easily understood description of how such a model works and discuss how it can be applied to real data. Model assumptions are discussed and the process of selecting a working correlation structure is thoroughly discussed. The steps necessitating collaboration between statistical and scientific investigators have been highlighted, as have considerations for missing data or uneven follow-up.

The results from a completed longitudinal cardiac imaging study were considered for illustration purposes. The data comes from a clinical trial for medical therapy for patients with mitral regurgitation, with repeated measurements taken at sixteen locations from the left ventricle to measure disease progression. The spatiotemporal correlation model was compared to previously used summary measures to demonstrate improved power as well as increased flexibility in the use of time- and space-varying predictors.


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Brandon George. Thomas Denney, Jr.. Himanshu Gupta. Louis Dell’Italia. Inmaculada Aban. "Applying a spatiotemporal model for longitudinal cardiac imaging data." Ann. Appl. Stat. 10 (1) 527 - 548, March 2016.


Received: 1 October 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

MathSciNet: MR3480506
Digital Object Identifier: 10.1214/16-AOAS911

Keywords: Correlation , imaging , separable , Spatiotemporal , summary measures

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 1 • March 2016
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