A Class of Weighted Log-Rank Tests for Survival Data When the Event is Rare
稀有事件生存数据的一类加权对数秩检验AI 解读 ↓
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稀有事件生存数据的一类加权对数秩检验
- 研究问题
- 当随访终点的事件数相对样本量很少时，如何改造趋近普通对数秩检验的原加权检验族，以适应稀有事件生存数据？
- 方法与思路
- 作者在稀有事件渐近框架下修改加权检验族，推导标准化统计量的正态极限，并通过连续备择和模拟分析效率。
- 材料陈述的贡献
- 摘要称对各权重参数可找到使修改检验渐近有效的特定转换模型，模拟显示可能显著增效，并给出更广检验族与回归扩展。
- 阅读关注点
- 应核对稀有事件的渐近定义、权重修改和删失条件，区分特定局部备择下的有效性与模拟中的增效，并检查回归扩展。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要；具体渐近条件和模拟设定未提供，效率优势限于所述模型与场景，不能断言普遍占优。
原文摘要
In many epidemiological and medical follow-up studies, a majority of study subjects do not experience the event of interest during the follow-up period. An important example is the ongoing prostate, lung, colorectal, and ovarian cancer Screening trial of the National Cancer Institute. In such a situation, the widely used Gp family of weighted log-rank statistics essentially reduces to the special case of the (unweighted) log-rank statistics. We propose a simple modification to the Gp family that adapts to survival data with rare events, a concept that we formulate in terms of a small number of events at the study endpoint relative to the sample size. The usual asymptotic properties, including convergence in distribution of the standardized statistics to the standard normal, are obtained under the rare event formulation. Semiparametric transformation models forming sequences of contiguous alternatives are considered and, for each p, a specific such model is identified so that the corresponding modified Gp statistic is asymptotically efficient. Simulation studies show that the proposed statistics do behave differently from the original Gp statistics when the event rate during the study period is low and the former could lead to a substantial efficiency gain over the latter. Extensions to the Gp γ family and to the regression problem are also given.