Inference for treatment-specific survival curves using machine learning
利用机器学习推断处理特定生存曲线AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究观察数据中处理特定生存曲线的因果识别、估计与推断。
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利用机器学习推断处理特定生存曲线
- 研究问题
- 在没有随机试验时，如何利用观察数据估计全体研究对象分别接受或不接受处理时的生存曲线，并对整条曲线进行统计推断？
- 方法与思路
- 作者提出交叉拟合的双重稳健估计量，用数据自适应方法估计条件生存函数，并设计集成学习器组合多个候选估计方法。
- 材料陈述的贡献
- 摘要称，给出了估计量逐点及时间上一致的相合性与渐近线性条件，兼容离散、连续及混合事件时间，并开展数值和手术应用研究。
- 阅读关注点
- 应核对因果识别与删失处理条件、辅助估计量的收敛要求，以及逐点和一致推断的区别与集成学习器的具体构造。
- 阅读边界
- 这是预印本，同行评审或期刊接收未经核实。摘要提及所有混杂均被观测，但未列出完整条件，也未报告覆盖率或应用效应数值。
原文摘要
In the absence of data from a randomized trial, researchers often aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome. In this context, interest often focuses on the treatment-specific survival curves; that is, the survival curves were the entire population under study to be assigned to receive the treatment or not. Under certain causal conditions, including that all confounders of the treatment-outcome relationship are observed, the treatment-specific survival can be identified with a covariate-adjusted survival function. Several estimators of this function have been proposed, including estimators based on outcome regression, inverse probability weighting, and doubly robust estimators. In this article, we propose a new cross-fitted doubly-robust estimator that incorporates data-adaptive (e.g. machine learning) estimators of the conditional survival functions. We establish conditions on the nuisance estimators under which our estimator is consistent and asymptotically linear, both pointwise and uniformly in time. We also propose a novel ensemble learner for combining multiple candidate estimators of the conditional survival estimators. Notably, our methods and results accommodate events occurring in discrete or continuous time (or both). We investigate the practical performance of our methods using numerical studies and an application to the effect of a surgical treatment to prevent metastases of parotid carcinoma on mortality.