AI 中文总结
针对区间删失数据预测难题，提出新型迁移学习方法，通过精心设计的惩罚转移生存概率信息，借助期望最大化算法高效计算，开发数据自适应聚合程序，经理论分析、模拟研究及实际应用验证了该方法的有效性。
AI 中文摘要
在慢性病研究中，当删失区间宽且随访有限时，对区间删失数据进行准确预测极具挑战性。虽源研究的辅助信息可改善目标研究中的预测，但现有迁移学习方法对模型或参数相似性有严格假设，或需访问个体层面的源数据。我们提出一种用于区间删失数据的新型迁移学习方法，允许任意源模型且无需共享源数据。该方法通过精心设计的惩罚从源研究中转移生存概率信息，并通过简单的期望最大化算法实现高效计算。当有多个源研究且其信息量未知时，我们进一步开发了一种对负迁移具有鲁棒性的数据自适应聚合程序。理论分析表明，只要至少有一个源研究信息充分，所提出的估计器比仅基于目标数据的估计器具有更快的收敛速度。广泛的模拟研究和对阿尔茨海默病神经影像学倡议数据的应用证明了我们方法的有效性。
英文摘要
Accurate prediction with interval-censored data is particularly challenging when censoring intervals are wide and follow-up is limited, as is common in studies of chronic diseases. Although auxiliary information from source studies may improve prediction in a target study, existing transfer learning methods typically impose restrictive assumptions on model or parameter similarity, or require access to individual-level source data. We propose a novel transfer learning method for interval-censored data that allows arbitrary source models and avoids sharing source data. Our approach transfers survival probability information from source studies through a carefully designed penalty and enables efficient computation via a simple EM algorithm. When multiple source studies are available and their informativeness is unknown, we further develop a data-adaptive aggregation procedure that is robust to negative transfer. Theoretical analysis shows that the proposed estimator attains a faster convergence rate than the target-only estimator whenever at least one source study is sufficiently informative. Extensive simulation studies and an application to data from the Alzheimer's Disease Neuroimaging Initiative demonstrate the effectiveness of our approach.
