Transfer learning estimation of the accelerated failure time model based on high-dimensional data

Motivated by a study on seriously ill hospitalized adults to improve their end-of-life care, we consider estimation of the accelerated failure time model, one of the most commonly used models for regression analysis of failure time data. Although many methods have been developed for the problem, standard approaches may fail or underperform when available information is limited. To address this issue, we propose two transfer learning estimation procedures that leverage auxiliary information from multiple source datasets. The first is a data-driven source detection procedure that classifies the source datasets into positively and negatively transferable groups and performs estimation using only the positively transferable or informative source datasets. The other is an ensemble-based approach that adaptively assigns weights to source datasets based on their relevance to the target dataset. Theoretical justifications are provided for the proposed methods, and an extensive simulation study is performed, indicating that the proposed methods work well in practice. Finally, they are applied to the study above and identify some prognostic factors that would not be possible by using the existing methods.