Article

An RMST-Integrated Machine Learning Framework for Interpretable Survival Analysis Under Non-Proportional Hazards: Application to the METABRIC Cohort

2026-03-10

Abstract excerpt

<h4>Background: </h4> Advances in machine learning (ML) based survival modeling enable the analysis of high-dimensional biomedical data. However, many approaches rely on the proportional hazards (PH) assumption, which is frequently violated in oncology and can limit the interpretability of hazard ratio–based results. Using Estrogen Receptor (ER) status in the METABRIC breast cancer cohort as a case study, we propo...

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Literature Corpus work: 7f535965-9543-58f7-a542-7a11c8a8f296
DOI: 10.20944/preprints202603.0802.v1

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