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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Molei Liu & Jiehuan Sun & Jose D. Herazo-Maya & Naftali Kaminski & Hongyu Zhao, 2019. "Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 614-629, December.
  • Handle: RePEc:spr:stabio:v:11:y:2019:i:3:d:10.1007_s12561-019-09256-0
    DOI: 10.1007/s12561-019-09256-0
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    References listed on IDEAS

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    Cited by:

    1. Kang, Kai & Song, Xinyuan, 2022. "Consistent estimation of a joint model for multivariate longitudinal and survival data with latent variables," Journal of Multivariate Analysis, Elsevier, vol. 187(C).

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