A Bayesian Approach to Causal Cure Models
因果治愈模型的贝叶斯方法AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究治疗对治愈比例及潜在主分层生存情况的因果效应，直接对应因果推断方向。
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因果治愈模型的贝叶斯方法
- 研究问题
- 当部分患者永远不会发生目标事件时，如何分别评估治疗对治愈比例及不同潜在治愈分层中生存情况的因果影响？
- 方法与思路
- 作者采用主分层与贝叶斯模型框架，估计治愈比例及始终未治愈分层的效应，并提出非始终治愈分层并集的生存效应。
- 材料陈述的贡献
- 摘要称，该框架统一了因果效应估计与经典混合治愈模型量，并通过模拟和术后呼吸衰竭随机试验展示其表现。
- 阅读关注点
- 应核对主分层效应的识别条件、删失与随访期限的处理、先验敏感性，以及生存平台是否足以支持治愈比例估计。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明识别条件，不能据此确认其稳健性范围。
原文摘要
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.