Evaluating the Use of Spatial Covariance Structures in the Analysis of Cardiovascular Imaging Data

Seals, Samantha R.; Katholi, Charles R.; Zhang, Jing; Aban, Inmaculada

doi:10.1080/03610918.2014.925922

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…The phrase cardiac imaging covers a wide variety of medical applications, from perfusion of oxygenated blood [Bowman and Waller (2004)] to the mechanical properties of the myocardium [Seals et al (2014)] to the structure of the left ventricle (as in Figure 1) that we will consider in this paper. Most analyses of the left ventricle via cardiac MRI scans in recent years have been based on the spatial model proposed by the American Heart Association (AHA) [Cerqueria et al (2002)].…”

Section: Clinical Applicationmentioning

confidence: 99%

“…It also means that there is not as much unique information among the segments, so not as much actual information is lost in the summarization. Last, there have only been a small number of studies [Bowman and Waller (2004), Seals et al (2014)] that have looked to introduce spatial analysis to cardiac imaging data, so it has not yet become the standard research approach in the field.…”

Section: Statistical Modelmentioning

confidence: 99%

“…The outer ring corresponds to the base, the middle ring to the mid, and the inner circle to the apex [adapted from Cerqueria et al (2002), Seals et al (2014), George and Aban (2015)]. The right region corresponds to the segments supplied by the left circumflex (LCX), the upper left those supplied by the left anterior descending (LAD), and the bottom left those supplied by the right coronary artery (RCA).…”

Section: Figmentioning

confidence: 99%

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Applying a spatiotemporal model for longitudinal cardiac imaging data

et al. 2016

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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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Section: Clinical Applicationmentioning

confidence: 99%

Section: Statistical Modelmentioning

confidence: 99%