survkl: an R package for transfer-learning-based integrated Cox models

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ID: 326362
2026
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Abstract
Abstract Summary Survival risk prediction often suffers from challenges such as rare event rates, small effective sample sizes, high-dimensional feature spaces, weak signals, population heterogeneity, and concerns over patient privacy. To overcome these obstacles and improve the precision of prognostic modeling, we introduce the survkl software, which enables the incorporation of external summary-level information with newly collected time-to-event data to support more robust and accurate predictions in survival analysis. Our method adaptively adjusts the weight given to external information, down-weighting heterogeneous information and highlighting more informative ones. The proposed tool accommodates both low-dimensional and high-dimensional data, offering unpenalized estimation and computationally efficient lasso, ridge, and elastic net penalties. The survkl software also provides auxiliary evaluation and plotting functions for model assessment. Availability and implementation survkl is freely available to the public at https://github.com/UM-KevinHe/survkl and published under General Public License version 3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Reference Key
openalex_W7204169279 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yubo Shao, Lingfeng Luo, Xiaohan Liu, Junyi Qiu, Di Wang, Kevin He
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag234
URL
https://doi.org/10.1093/bioadv/vbag234
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