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<p>Advances in high-throughput technologies have vastly expanded the ability to dynamically characterize disease states and associated biomarkers, which play a crucial role in the prevention, detection, and treatment of diseases. In the field of precision medicine, pinpointing patient subgroups that stand to gain the most from specific treatments is of paramount interest. This study explores the challenge of identifying such subpopulations, characterized by disease outcome-biomarker relationship. This complexity is due not only to the dynamic nature of disease outcomes and biomarker profiles but also to the intricate and often nonlinear—interactions between them, necessitating careful consideration. This study employs methods from reproducing kernel Hilbert space (RKHS) to model the complex interactions between outcomes and biomarkers. By utilizing RKHS distance metrics, the authors identify clusters according to varying patterns in the estimated subject-specific outcome-biomarker relationship functions. Comprehensive numerical simulations are conducted to validate the superior efficacy of the proposed approach in comparison to existing methodologies. Additionally, the utility of the proposed method is further exemplified through its application to real-world datasets.</p>

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Subgroup Identification by Supervised Clustering Analysis of Disease Outcomes and Biomarkers with Repeated Measurements

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Yicong Mao,
  • Yunhan Mou,
  • Xiang Zhan,
  • Yuan Huang

摘要

Advances in high-throughput technologies have vastly expanded the ability to dynamically characterize disease states and associated biomarkers, which play a crucial role in the prevention, detection, and treatment of diseases. In the field of precision medicine, pinpointing patient subgroups that stand to gain the most from specific treatments is of paramount interest. This study explores the challenge of identifying such subpopulations, characterized by disease outcome-biomarker relationship. This complexity is due not only to the dynamic nature of disease outcomes and biomarker profiles but also to the intricate and often nonlinear—interactions between them, necessitating careful consideration. This study employs methods from reproducing kernel Hilbert space (RKHS) to model the complex interactions between outcomes and biomarkers. By utilizing RKHS distance metrics, the authors identify clusters according to varying patterns in the estimated subject-specific outcome-biomarker relationship functions. Comprehensive numerical simulations are conducted to validate the superior efficacy of the proposed approach in comparison to existing methodologies. Additionally, the utility of the proposed method is further exemplified through its application to real-world datasets.