Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival
以无模型方法评估删失中间结局对总生存期的替代作用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕治疗效应及其被替代结局解释的比例展开，直接涉及因果效应的替代结局评价。
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以无模型方法评估删失中间结局对总生存期的替代作用
- 研究问题
- 当替代结局和主要结局均为受到删失的事件时间时，如何评价较早观测到的替代信息能够解释多少治疗对总体生存的效应？
- 方法与思路
- 作者定义删失替代结局解释的治疗效应比例，并通过替代信息的最优变换构造非参数估计方法，以总体生存差异作为目标治疗效应。
- 材料陈述的贡献
- 摘要称，该方法无需参数模型，并以较弱条件保证真实解释比例位于零与一之间；模拟和转移性结直肠癌数据用于展示有限样本表现。
- 阅读关注点
- 应核对总体生存差异及解释比例的精确定义、最优变换的构造、删失处理与比例取值保证所需条件，并查看应用中的评估方式。
- 阅读边界
- 所用摘要来自 arXiv 预印本，同行评审及期刊接收未核实；无模型不等于无假设，摘要未说明删失条件，也未给出替代有效性的具体结果。
原文摘要
Clinical trials or studies oftentimes require long-term and/or costly follow-up of participants to evaluate a novel treatment/drug/vaccine. There has been increasing interest in the past few decades in using short-term surrogate outcomes as a replacement of the primary outcome i.e., in using the surrogate outcome, which can potentially be observed sooner, to make inference about the treatment effect on the long-term primary outcome. Very few of the available statistical methods to evaluate a surrogate are applicable to settings where both the surrogate and the primary outcome are time-to-event outcomes subject to censoring. Methods that can handle this setting tend to require parametric assumptions or be limited to assessing only the restricted mean survival time. In this paper, we propose a non-parametric approach to evaluate a censored surrogate outcome, such as time to progression, when the primary outcome is also a censored time-to-event outcome, such as time to death, and the treatment effect of interest is the difference in overall survival. Specifically, we define the proportion of the treatment effect on the primary outcome that is explained (PTE) by the censored surrogate outcome in this context, and estimate this proportion by defining and deriving an optimal transformation of the surrogate information. Our approach provides the added advantage of relaxed assumptions to guarantee that the true PTE is within (0,1), along with being model-free. Finite sample performance of our estimators are illustrated via extensive simulation studies and a real data application examining progression-free survival as a surrogate for overall survival for patients with metastatic colorectal cancer.