A0316
Title: Transfer learning under the Cox model with interval-censored data
Authors: Mengqi Xie - Capital Normal University (China) [presenting]
Tao Hu - Capital Normal University (China)
Jie Zhou - Capital Normal University (China)
Abstract: Transfer learning, focusing on information borrowing to address limited sample size issues, has gained increasing attention in recent years. The method aims to utilize data from other population groups as a complement to enhance risk factor discernment and failure time prediction among underrepresented subgroups. However, a literature gap exists in effective knowledge transfer from the source to the target for risk assessment with interval-censored data while accommodating population incomparability and privacy constraints. The objective is to bridge this gap by developing a transfer learning approach under the Cox proportional hazards model. The tuning-free Trans-Cox-MIC algorithm is introduced, enabling adaptable information sharing in regression coefficients and baseline hazards while ensuring computational efficiency. Extensive simulations showcase the method's accuracy, robustness, and efficiency. Application to the prostate cancer screening data demonstrates enhanced risk estimation precision and predictive performance in the African American population.
