The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 1 (2016), 527-548.
Applying a spatiotemporal model for longitudinal cardiac imaging data
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.
Ann. Appl. Stat., Volume 10, Number 1 (2016), 527-548.
Received: October 2015
First available in Project Euclid: 25 March 2016
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George, Brandon; Denney, Jr., Thomas; Gupta, Himanshu; Dell’Italia, Louis; Aban, Inmaculada. Applying a spatiotemporal model for longitudinal cardiac imaging data. Ann. Appl. Stat. 10 (2016), no. 1, 527--548. doi:10.1214/16-AOAS911. https://projecteuclid.org/euclid.aoas/1458909926
- Supplementary tables and figure. We provide QQ plots of the model residuals justifying the log transformation, and tables of the regression parameters for the primary spatiotemporal model and inference about a treatment-by-time interaction when the post-surgery observations were excluded from the analysis.