In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events, and survival data while accounting for their dependencies is critical. While joint models for the three components exist in statistical literature, many of these approaches are limited by heavy parametric assumptions and scalability issues. Recently, incorporating deep learning techniques into joint modeling has shown promising results. However, current methods only address joint modeling of longitudinal measurements at regularly-spaced observation times and survival events, neglecting recurrent events. In this paper, we develop TransformerLSR, a flexible transformer-based deep modeling and inference framework to jointly model all three components simultaneously. TransformerLSR integrates deep temporal point processes into the joint modeling framework, treating recurrent and terminal events as two competing processes dependent on past longitudinal measurements and recurrent event times. Additionally, TransformerLSR introduces a novel trajectory representation and model architecture to potentially incorporate a priori knowledge of known latent structures among concurrent longitudinal variables. We demonstrate the effectiveness and necessity of TransformerLSR through simulation studies and analyzing a real-world medical dataset on patients after kidney transplantation.

本文介绍了TransformerLSR，一种基于Transformer的深度建模和推断框架，用于同时联合建模长期测量、重复事件和生存数据的三个组成部分，通过整合深度时间点过程到联合建模框架中，并将重复事件和终端事件看作两个依赖于过去的长期测量和重复事件时间的竞争过程。此外，TransformerLSR引入了一种新颖的轨迹表示和模型架构，可以潜在地结合已知的潜在结构的先验知识。通过仿真研究和对肾移植后患者的真实医疗数据进行分析，我们证明了TransformerLSR的有效性和必要性。

TransformerLSR: 关注纵向数据、生存和复发事件以及并发潜在结构的联合模型