Abstract
Longitudinal genomics data and survival outcome are common in biomedical studies, where the genomics data are often of high dimension. It is of great interest to select informative longitudinal biomarkers (e.g. genes) related to the survival outcome. In this paper, we develop a computationally efficient tool, LCox, for selecting informative biomarkers related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome. We show that LCox has improved performance compared to existing methods through extensive simulation studies. In addition, by applying LCox to a dataset of patients with idiopathic pulmonary fibrosis, we are able to identify biologically meaningful genes while all other methods fail to make any discovery. An R package to perform LCox is freely available at https://CRAN.R-project.org/package=LCox.
Funding source: National Institutes of Health
Award Identifier / Grant number: R01 GM59507, P01 CA154295, U01 HL112707, R01 HL127349, U01 HL108642, and UH3 HL123886
Funding source: NSF
Award Identifier / Grant number: DMS-15-12975
Funding statement: Jiehuan Sun and Hongyu Zhao were supported in part by the National Institutes of Health Funder Id 10.13039/100000002, grants R01 GM59507 and P01 CA154295. Jose D. Herazo-Maya was supported by the Harold Amos Faculty development program of the Robert Wood Johnson Foundation and the Pulmonary Fibrosis Foundation. Naftali Kaminski was supported in part by the National Institutes of Health grants U01 HL112707, R01 HL127349, U01 HL108642, and UH3 HL123886. The research of Jane-Ling Wang was supported in part by the NSF grant DMS-15-12975.
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The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/sagmb-2017-0060).
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