Transfer learning, focusing on information borrowing to address limited sample size issues, has gained increasing attention in recent years. Our 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. Our objective is to bridge this gap by developing a transfer learning approach under the Cox proportional hazards model. We introduce the tuning‐free Trans‐Cox‐MIC algorithm, enabling adaptable information sharing in regression coefficients and baseline hazards, while ensuring computational efficiency. Our approach accommodates covariate distribution shifts, coefficient variations, and baseline hazard discrepancies. 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.

中文翻译：

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Cox 模型下的区间删失数据迁移学习

迁移学习专注于信息借用来解决样本量有限的问题，近年来受到越来越多的关注。我们的方法旨在利用其他人群的数据作为补充，以增强代表性不足的亚组中的风险因素识别和故障时间预测。然而，在利用区间审查数据进行风险评估时，在从源到目标的有效知识转移，同时适应人口不可比性和隐私限制方面，存在文献差距。我们的目标是通过在 Cox 比例风险模型下开发迁移学习方法来弥补这一差距。我们引入了免调优的 Trans-Cox-MIC 算法，实现了回归系数和基线风险的适应性信息共享，同时确保了计算效率。我们的方法适应协变量分布变化、系数变化和基线风险差异。广泛的模拟展示了该方法的准确性、稳健性和效率。前列腺癌筛查数据的应用表明，非洲裔美国人群体的风险估计精度和预测性能得到了提高。