标题： 一种用于交替复发事件的随机森林逆概率加权伪观测框架 显示英文标题

标题： A Random Forest Inverse Probability Weighted Pseudo-Observation Framework for Alternating Recurrent Events

摘要： 交替的重复事件，其中受试者随时间推移经历两种可能相关的事件类型，在医疗、社会和行为研究中很常见。 通常有一个感兴趣的首要事件，当该事件触发时，会启动一个治疗和恢复期，通过次级事件发生时间来衡量。 例如，癌症患者可能会经历多次需要住院的血栓紧急情况，随后出院；酗酒障碍患者可能会经历成瘾和戒断期；照护者可能会经历抑郁和恢复期。 数据的潜在删失需要特别处理。 此外，当个体已经触发首要事件但尚未达到后续的次级事件时，还存在首要事件类型的缺失风险期。 在本文中，我们开发了一个对删失交替重复事件进行回归分析的框架，该框架使用随机森林反概率加权策略，以避免由于替代次级状态的信息性缺失而导致首要事件发生时间分析中的偏差。 所提出的回归模型在考虑删失复杂性的前提下，估计了$\tau$-限制的首要事件发生时间。 模拟结果显示，当替代事件发生时间独立或相关时，我们的方法表现良好。 我们分析了一项移动健康研究数据，以评估自我护理推送通知对创伤性脑损伤患者照护者心理状态的影响。 显示英文摘要

摘要： Alternating recurrent events, where subjects experience two potentially correlated event types over time, are common in healthcare, social, and behavioral studies. Often there is a primary event of interest that, when triggered, initiates a period of treatment and recovery measured via a secondary time-to-event. For example, cancer patients can experience repeated blood clotting emergencies that require hospitalization followed by discharge, people with alcohol use disorder can have periods of addiction and sobriety, or care partners can experience periods of depression and recovery. Potential censoring of the data requires special handling. Overlaying this are the missing at-risk periods for the primary event type when individuals have initiated the primary event but not reached the subsequent secondary event. In this paper, we develop a framework for regression analysis of censored alternating recurrent events that uses a random forest inverse probability weighting strategy to avoid bias in the analysis of the time to the primary event due to informative missingness from the alternate secondary state. The proposed regression model estimates $\tau$-restricted mean time to the primary event of interest while taking into account complexities of censored. Simulations show good performance of our method when the alternate times-to-event are either independent or correlated. We analyze a mobile health study data to evaluate the impact of self-care push notifications on the mental state of caregivers of traumatic brain injury patients.