用于复发性事件动态风险预测的随机森林：一种伪观测方法
Random Forest for Dynamic Risk Prediction or Recurrent Events: A Pseudo-Observation Approach
AI总结：
本文提出一种基于伪观测的随机森林方法，从重建的删失纵向数据中动态预测复发性事件患者在后续τ时长内保持无事件的概率，并在复杂关联场景及慢阻肺急性加重数据上验证了其优于现有复发性事件建模框架的预测能力。
摘要：
复发性事件在临床、医疗保健、社会和行为研究中十分常见。针对可能存在删失的复发性事件数据，一种近期的分析框架是构建一个删失纵向数据集，该数据集由多个预先指定的长度为 $τ$ 的随访窗口内首次复发性事件发生时间组成。随着遗传学、组学和电子健康记录来源产生数量惊人的潜在预测因子，随机森林等机器学习方法日益受到欢迎，因为它们能够纳入来自高度相关且具有非标准关系的预测因子的信息。在本文中，我们通过开发一种随机森林方法来弥合这一差距，该方法用于从重建的删失纵向数据集中动态预测在随后的 $τ$ 时长随访期内保持无事件的概率。我们证明，在预测因子与复发性事件结局之间的关联本质上较为复杂的情况下，与 Xia 等人（2020）的复发性事件建模框架相比，我们的随机森林算法在预测 $τ$ 时长随访期内保持无事件的概率方面具有更强的能力。所提出的随机森林算法使用来自阿奇霉素预防慢性阻塞性肺疾病急性加重研究（Albert 等人，2011）的复发性加重数据进行了演示。
英文摘要：
Recurrent events are common in clinical, healthcare, social and behavioral studies. A recent analysis framework for potentially censored recurrent event data is to construct a censored longitudinal data set consisting of times to the first recurrent event in multiple prespecified follow-up windows of length $τ$. With the staggering number of potential predictors being generated from genetic, -omic, and electronic health records sources, machine learning approaches such as the random forest are growing in popularity, as they can incorporate information from highly correlated predictors with non-standard relationships. In this paper, we bridge this gap by developing a random forest approach for dynamically predicting probabilities of remaining event-free during a subsequent $τ$-duration follow-up period from a reconstructed censored longitudinal data set. We demonstrate the increased ability of our random forest algorithm for predicting the probability of remaining event-free over a $τ$-duration follow-up period when compared to the recurrent event modeling framework of Xia et al. (2020) in settings where association between predictors and recurrent event outcomes is complex in nature. The proposed random forest algorithm is demonstrated using recurrent exacerbation data from the Azithromycin for the Prevention of Exacerbations of Chronic Obstructive Pulmonary Disease (Albert et al., 2011).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
