Longitudinal imaging studies allow great insight into how the structure and function of a subject’s internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient’s body.
Dr. Brandon J. George, prior graduate student in the department of biostatistics, at the University of Alabama at Birmingham — along with Dr. Inmaculada B. Aban, professor, also in UAB’s department of biostatistics — recently proposed the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data.
The simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients at UAB, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates when the specified covariance model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Additionally, misspecification of the covariance structure was found to have the ability to inflate the type I error or to reduce statistical power when the working correlation structure is a poor fit for the true underlying correlation.
In the study, the researchers provide an example with clinical data to illustrate how covariance structure selection can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects.
“Selecting a Separable Parametric Spatiotemporal Covariance Structure for Longitudinal Imaging Data” was published in October in the journal Statistics in Medicine.
Journal article: http://onlinelibrary.wiley.com/doi/10.1002/sim.6324/abstract