具有固化子群的时间到事件数据的因果推理。

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-03-27 DOI:10.1093/biomtc/ujae028
Yi Wang, Yuhao Deng, Xiao-Hua Zhou
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引用次数: 0

摘要

在研究治疗对时间到事件结果的影响时,常见的情况是有些人从未发生过失败事件,这表明他们已经治愈。然而,由于普查的原因,可能无法观察到治愈状态,这就给确定治疗效果带来了挑战。目前的方法主要侧重于估计各种治愈模型的模型参数,最终导致缺乏因果解释。为解决这一问题,我们提出了基于主分层的始终未治愈者的两个因果估计值,即时间风险差异和平均生存时间差异,作为治疗效果对治愈率的补充。通过这些估计值,我们可以研究治疗对始终未治愈亚群中失败时间的影响。我们展示了在可忽略的治疗分配机制下,使用潜在治愈状态的替代变量的可识别性,这两个估计值是可识别的。我们还提供了使用混合治愈模型的估计方法。我们将我们的方法应用于一项观察性研究,该研究比较了不同移植类型治愈急性淋巴细胞白血病的无白血病生存率。我们提出的方法得出了具有洞察力的结果,可为未来的治疗决策提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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