AI 中文总结
针对生存时间数据中含治愈个体的情况，提出基于贝叶斯模型的方法，考虑主分层因果估计量及新估计量，通过模拟验证可靠性，并应用于随机试验展示实用性，用于分析治疗对治愈比例和未治愈亚群生存函数的因果效应。
AI 中文摘要
生存时间数据常包含未经历失败事件的个体即治愈个体。在临床研究中，很大比例患者在观察期内无事件发生，出现生存平台期，这常被视为存在治愈比例的证据。分析此类数据需推断治愈比例和未治愈亚群的生存函数，传统上用混合治愈模型。评估治疗对这些量的因果效应并不简单。本文考虑主分层因果估计量，还引入新估计量。从基于贝叶斯模型的角度构建问题，通过模拟验证该方法可靠性，最后通过应用于腹部手术后低氧性呼吸衰竭患者非侵入性通气与标准氧疗随机试验说明其实用性。
英文摘要
Time-to-event data often include individuals who will never experience the failure event, and are therefore considered cured. In such settings, frequently encountered in clinical research, a substantial proportion of patients may remain event-free throughout the observation period, leading to the appearance of a survival plateau that is commonly interpreted as evidence of a cured fraction. Analysing such data requires inference on the cure fraction and on the survival function for the uncured subpopulation, tasks which are traditionally achieved with mixture cure models. However, assessing the causal effect of a treatment on these quantities is non-trivial. We consider principal stratification causal estimands, which have been proposed to evaluate effects on the cure fraction and on the survival for an always-uncured stratum. We additionally introduce a novel estimand, which considers the causal effect on the survival for a non-always-cured union of strata. We frame the problem from a Bayesian model-based perspective, which provides a flexible and unified estimation strategy while maintaining a direct link with classical mixture cure model quantities. The reliability of the proposed approach is validated through simulations, demonstrating competitive and robust performance relative to existing methods. Finally, we illustrate its practical usefulness through an application to a randomized trial comparing non-invasive ventilation with standard oxygen therapy in patients with hypoxemic respiratory failure following abdominal surgery.
