Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension

Molei Liu, Jiehuan Sun, Jose D. Herazo-Maya, Naftali Kaminski and Hongyu Zhao ()
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Molei Liu: Harvard University
Jiehuan Sun: Yale University
Jose D. Herazo-Maya: Yale School of Medicine
Naftali Kaminski: Yale School of Medicine
Hongyu Zhao: Yale University

Statistics in Biosciences, 2019, vol. 11, issue 3, No 7, 614-629

Abstract: Abstract Joint models for longitudinal biomarkers and time-to-event data are widely used in longitudinal studies. Many joint modeling approaches have been proposed to handle different types of longitudinal biomarkers and survival outcomes. However, most existing joint modeling methods cannot deal with a large number of longitudinal biomarkers simultaneously, such as the longitudinally collected gene expression profiles. In this article, we propose a new joint modeling method under the Bayesian framework, which is able to analyze longitudinal biomarkers of high dimension. Specifically, we assume that only a few unobserved latent variables are related to the survival outcome and the latent variables are inferred using a factor analysis model, which greatly reduces the dimensionality of the biomarkers and also accounts for the high correlations among the biomarkers. Through extensive simulation studies, we show that our proposed method has improved prediction accuracy over other joint modeling methods. We illustrate the usefulness of our method on a dataset of idiopathic pulmonary fibrosis patients in which we are interested in predicting the patients’ time-to-death using their gene expression profiles.

Keywords: Bayesian factor analysis; Joint models; Longitudinal biomarkers of high dimension; Survival prediction (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-019-09256-0

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