交替复发事件的随机森林逆概率加权伪观测框架
A Random Forest Inverse Probability Weighted Pseudo-Observation Framework for Alternating Recurrent Events
AI总结：
针对交替复发事件中主要事件时间因次要状态信息性缺失而偏倚的问题，提出随机森林逆概率加权伪观测框架，估计τ-限制平均时间，模拟验证有效并应用于移动健康数据。
摘要：
交替复发事件在医疗、社会和行为研究中很常见，其中受试者会随时间经历两种可能相关的事件类型。通常存在一个感兴趣的主要事件，当其触发时，会启动一段通过次要时间到事件测量的治疗和恢复期。例如，癌症患者可能反复出现需要住院随后出院的血液凝块紧急情况；酒精使用障碍患者可能经历成瘾和清醒的时期；护理伙伴可能经历抑郁和恢复的时期。数据的潜在删失需要特殊处理。此外，当个体已启动主要事件但尚未到达随后的次要事件时，主要事件类型的风险期缺失问题叠加其上。在本文中，我们开发了一个用于删失交替复发事件回归分析的框架，该框架使用随机森林逆概率加权策略，以避免由于交替次要状态的信息性缺失而导致的主要事件时间分析中的偏差。所提出的回归模型在考虑删失复杂性的同时，估计到主要感兴趣事件的$\ au$-限制平均时间。模拟显示，当交替时间到事件独立或相关时，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护理人员心理状态的影响。
英文摘要：
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 $τ$-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.
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。
