Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866909981045948416 |
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| author | Li, Xingyu Liu, Qing Jiang, Tony Xia, Hong Amy Wei, Peng Hobbs, Brian P. |
| author_facet | Li, Xingyu Liu, Qing Jiang, Tony Xia, Hong Amy Wei, Peng Hobbs, Brian P. |
| contents | Precision oncology aims to prescribe the optimal cancer treatment to the right patients, maximizing therapeutic benefits. However, identifying patient subgroups that may benefit more from experimental cancer treatments based on randomized clinical trials presents a significant analytical challenge. To address this, we introduce a novel unsupervised machine learning approach based on very dense random survival forests (up to 100,000 trees), equipped with a new splitting rule that explicitly targets treatment-effect heterogeneity. This method is robust, interpretable, and effectively identifies responsive subgroups. Extensive simulations confirm its ability to detect heterogeneous patient responses and distinguish between datasets with and without heterogeneity, while maintaining a stringent Type I error rate of 1%. We further validate its performance using Phase III randomized clinical trial datasets, demonstrating significant patient heterogeneity in treatment response based on baseline characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01380 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit Li, Xingyu Liu, Qing Jiang, Tony Xia, Hong Amy Wei, Peng Hobbs, Brian P. Methodology Precision oncology aims to prescribe the optimal cancer treatment to the right patients, maximizing therapeutic benefits. However, identifying patient subgroups that may benefit more from experimental cancer treatments based on randomized clinical trials presents a significant analytical challenge. To address this, we introduce a novel unsupervised machine learning approach based on very dense random survival forests (up to 100,000 trees), equipped with a new splitting rule that explicitly targets treatment-effect heterogeneity. This method is robust, interpretable, and effectively identifies responsive subgroups. Extensive simulations confirm its ability to detect heterogeneous patient responses and distinguish between datasets with and without heterogeneity, while maintaining a stringent Type I error rate of 1%. We further validate its performance using Phase III randomized clinical trial datasets, demonstrating significant patient heterogeneity in treatment response based on baseline characteristics. |
| title | Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit |
| topic | Methodology |
| url | https://arxiv.org/abs/2601.01380 |