Two Transfer Learning Approaches for Regression Analysis of High-dimensional Interval-censored Failure Time Data

High-dimensional interval-censored failure time data occur in many areas and many methods have been proposed for their regression analysis. However, these methods may fail or not perform well when the available information is limited. To address this, we propose two transfer learning estimation procedures that can take into account multiple so-called source data under the framework of semiparametric linear transformation models, which are commonly used and well-known for their flexibility. The first one is a data-driven source detection procedure that allows one to classify the source data into two groups, positive and negative transfers, and perform the transfer learning estimation based on the combination of all of the positive transfers. Then a model-averaging approach is developed with the adaptive weights to source datasets determined based on their relevance to the target task. The asymptotic properties of the resulting estimators including the consistency are provided. An extensive simulation is conducted and demonstrates the superior performance of the proposed methods in terms of estimation accuracy and predictive capability. Finally they are applied to a breast cancer data that motivated this study.