Global and Episode-Specific Prediction of Recurrent Events Using Longitudinal Health Informatics Data

• 成果类型: Article; Early Access
• 署名作者: Sun, Yifei; Chiou, Sy Han; Huang, Chiung-Yu
• 署名单位: Columbia University; Southern Methodist University; University of California System; University of California San Francisco
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2497569
• 发表日期: 2025
• 关键词: seer-medicare data; regression-analysis; survival; trees

摘要

Accurate prediction of recurrent clinical events is crucial for effective management of chronic conditions such as cancer and cardiovascular disease. In recent years, longitudinal health informatics databases, which routinely collect data on repeated clinical events, have been increasingly used to construct risk prediction models. We introduce a novel nonparametric framework to predict recurrent events on a gap time scale using survival tree ensembles. Our framework incorporates two predictive modeling strategies: episode-specific model and global model. These models avoid strong assumptions on how future event risk depends on previous event history and other predictors, making them a promising alternative to Cox-type models. Additional complexities in tree-based prediction for recurrent events include induced informative censoring of gap times and inter-event correlations. We develop algorithms to address these issues through the use of inverse probability of censoring weighting and modified resampling procedures. Applied to SEER-Medicare data to predict repeated hospitalizations for breast cancer patients, our models showed superior performance. In particular, borrowing information across events via global models substantially improved prediction accuracy for later hospitalizations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.