Applying a spatiotemporal model for longitudinal cardiac imaging data

Abstract

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.

Does Testing More Frequently Shorten the Time to Detect Disease Progression?

“…In their analysis of longitudinal cardiac imaging data, George et al 12 consider several models for the temporal and the spatial correlations that can be expected across time and across different image locations. Lawton et al 13 consider a longitudinal model for disease progression of multiple sclerosis patients.…”

“…In their analysis of longitudinal perimetry data from glaucoma patients, Pathak et al 11 propose a structural model for the progression that includes the exponential of time and an autoregressive noise component that allows for the temporal correlation among adjacent observations. In their analysis of longitudinal cardiac imaging data, George et al 12 consider several models for the temporal and the spatial correlations that can be expected across time and across different image locations. Lawton et al 13 consider a longitudinal model for disease progression of multiple sclerosis patients.…”

Statistical Inference for Mean Function of Longitudinal Imaging Data over Complicated Domains

“…Although they found information criteria was highly accurate at choosing spatial and temporal parametric correlation functions, the risk of model misspecification and poor performance in inference remained inevitable. George et al (2016) described how to use the above model in practice and applied it for longitudinal cardiac imag-Statistica Sinica: Newly accepted Paper (accepted author-version subject to English editing) ing study. They restricted that a handful successive images with a small number of spatial locations were collected daily, monthly or even yearly in limited times, but now longitudinal imaging data usually comes in the form of magnitude order greater numbers of spatial (thousands of pixels) and temporal (multiple measures per day or hour) observations.…”

Spatial statistical modelling of capillary non-perfusion in the retina

“…Similarly George et al . 12 proposed a model of 16 sectors for the left ventricle. Lange 13 proposed a linear model with patterned correlated errors.…”