Nonparametric generalized fiducial inference for survival functions under censoring
删失下生存函数的非参数广义信赖推断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：摘要聚焦删失生存函数的通用统计推断，虽有治疗比较应用，但未明确提供因果或所列分布效应研究的依据。
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删失下生存函数的非参数广义信赖推断
- 研究问题
- 在右删失数据中，如何对非参数生存函数开展点态与整条曲线的区间推断，并用于单样本和两样本检验？
- 方法与思路
- 作者构造生存函数的广义信赖分布，据此建立点态和曲线置信区间及检验，并给出函数型 Bernstein–von Mises 定理。
- 材料陈述的贡献
- 摘要称，所提区间在部分渐近方法覆盖不足的情形下保持覆盖，且在一些场景中区间较短、检验功效较高。
- 阅读关注点
- 应核对删失机制、函数型定理的条件、曲线区间的构造，以及覆盖率、长度和功效比较分别对应哪些模拟场景。
- 阅读边界
- 仅据 Crossref 摘要；删失假设、定理条件和具体数值未说明，胃癌治疗比较本身不能据此解释为因果效应。
原文摘要
Summary Since the introduction of fiducial inference by Fisher in the 1930s, its application has been largely confined to relatively simple, parametric problems. In this paper, we present what might be the first time fiducial inference is systematically applied to estimation of a nonparametric survival function under right censoring. We find that the resulting fiducial distribution gives rise to surprisingly good statistical procedures applicable to both one-sample and two-sample problems. In particular, we use the fiducial distribution of a survival function to construct pointwise and curvewise confidence intervals for the survival function, and propose tests based on the curvewise confidence interval. We establish a functional Bernstein–von Mises theorem, and perform thorough simulation studies in scenarios with different levels of censoring. The proposed fiducial-based confidence intervals maintain coverage in situations where asymptotic methods often have substantial coverage problems. Furthermore, the average length of the proposed confidence intervals is often shorter than the length of confidence intervals for competing methods that maintain coverage. Finally, the proposed fiducial test is more powerful than various types of log-rank tests and sup log-rank tests in some scenarios. We illustrate the proposed fiducial test by comparing chemotherapy against chemotherapy combined with radiotherapy, using data from the treatment of locally unresectable gastric cancer.