Joint Modeling of Longitudinal Imaging and Survival Data

This article considers a joint modeling framework for simultaneously examining the dynamic pattern of longitudinal and ultrahigh-dimensional images and their effects on the survival of interest. A functional mixed effects model is considered to describe the trajectories of longitudinal images. Then,...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2023-04, Vol.32 (2), p.402-412
Main Authors: Kang, Kai, Song, Xin Yuan
Format: Article
Language:English
Subjects:
Alzheimer's disease
Dimensional analysis
Error analysis
HD-FPCA
Imaging data
Longitudinal response
Markov chains
MCMC methods
Medical imaging
Modelling
Principal components analysis
Regression models
Statistical analysis
Statistical inference
Survival
Time-to-event outcome
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Summary:This article considers a joint modeling framework for simultaneously examining the dynamic pattern of longitudinal and ultrahigh-dimensional images and their effects on the survival of interest. A functional mixed effects model is considered to describe the trajectories of longitudinal images. Then, a high-dimensional functional principal component analysis (HD-FPCA) is adopted to extract the principal eigenimages to reduce the ultrahigh dimensionality of imaging data. Finally, a Cox regression model is used to examine the effects of the longitudinal images and other risk factors on the hazard. A theoretical justification shows that a naive two-stage procedure that separately analyzes each part of the joint model produces biased estimation even if the longitudinal images have no measurement error. We develop a Bayesian joint estimation method coupled with efficient Markov chain Monte Carlo sampling schemes to perform statistical inference for the proposed joint model. A Monte Carlo dynamic prediction procedure is proposed to predict the future survival probabilities of subjects given their historical longitudinal images. The proposed model is assessed through extensive simulation studies and an application to Alzheimer's Disease Neuroimaging Initiative, which turns out to hold the promise of accuracy and possess higher predictive capacity for survival outcome compared with existing methods. Supplementary materials for this article are available online.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2022.2102027