Bayesian Learning of Personalized Longitudinal Biomarker Trajectory

Shouhao Zhou (), Xuelin Huang (), Chan Shen () and Hagop M. Kantarjian ()
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Shouhao Zhou: Pennsylvinia State University
Xuelin Huang: University of Texas M.D. Anderson Cancer Center
Chan Shen: Pennsylvinia State University
Hagop M. Kantarjian: University of Texas M.D. Anderson Cancer Center

Annals of Data Science, 2024, vol. 11, issue 3, No 13, 1050 pages

Abstract: Abstract This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.

Keywords: Bayesian multilevel modeling; Beta regression; Fractional polynomials; Longitudinal analysis; Precision medicine; Subject-specific prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00486-0

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