Rank-Based Transfer Learning for High-Dimensional Survival Data With Application to Sepsis Data

Sepsis remains a critical challenge due to its high mortality and complex prognosis. To address data limitations in studying MSSA sepsis, we extend existing transfer learning frameworks to accommodate transformation models for high-dimensional survival data. Specifically, we construct a measurement index based on the C-index for intelligently identifying the helpful source datasets, and the target model performance is improved by leveraging information from the identified source datasets via performing the transfer step and debiasing step. Another significant development is that statistical properties are rigorously established, including ${\ell }_1/{\ell }_2$ -estimation error bounds of the transfer learning algorithm and detection consistency property of the transferable source detection algorithm. Extensive simulations and analysis of MIMIC-IV sepsis data demonstrate the estimation and prediction accuracy, and practical advantages of our approach, providing significant improvements in survival estimates for MSSA sepsis patients.