A Comparison of Certain Bootstrap Confidence Intervals in the Cox Model
Cox模型中若干Bootstrap置信区间的比较AI 解读 ↓
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Cox模型中若干Bootstrap置信区间的比较
- 研究问题
- 在Cox比例风险模型中，不同重抽样方式与区间构造方法如何影响回归参数、生存函数及中位生存时间的区间表现？
- 方法与思路
- 作者交叉比较三种重抽样方式与百分位、混合及bootstrap-t区间，并在给定协变量和删失模式的蒙特卡洛设计中对照渐近区间。
- 材料陈述的贡献
- 摘要称渐近区间在所述模拟中最适合回归参数，却不适合另两类对象；重抽样方式与区间构造之间还存在交互影响。
- 阅读关注点
- 应核对比较标准、删失与协变量的固定方式，以及仅条件于协变量的抽样优于同时条件于删失模式这一结果的适用范围。
- 阅读边界
- 仅据版本未注明的OpenAlex索引摘要，不能推断刊本变化；方法优劣来自特定模拟设计，摘要未提供覆盖率、长度或参数设置。
原文摘要
We study bootstrap confidence intervals for three types of parameters in Cox's proportional hazards model: the regression parameter, the survival function at fixed time points, and the median survival time at fixed values of a covariate. Several types of bootstrap confidence intervals are studied, and the type of interval is determined by two factors. One factor is the method of drawing the bootstrap sample. We consider three such methods: (1) ordinary resampling from the empirical cumulative distribution function, (2) resampling conditional on the covariates, and (3) resampling conditional on the covariates and the censoring pattern. Another factor is the method of forming the confidence interval from a given sample; the methods considered are the percentile, hybrid, and bootstrap-t. All the methods of forming confidence intervals are compared to each other and to the standard asymptotic method via a Monte Carlo study. The data sets for this Monte Carlo study are simulated conditionally on the covariates and the censoring pattern, the situation appropriate for the third method of resampling. One conclusion drawn from the Monte Carlo study is that the asymptotic method is best for the regression parameter, but not for the survival function or the median survival time. Conclusions about the bootstrap methods include the surprising result that, overall, the second method of drawing the samples outperforms the third method. Also, there is an interaction effect between the two factors, method of drawing the sample and method of forming the interval, especially for estimation of the regression parameter. Finally, the bootstrap-t intervals are consistently outperformed by at least one of the two more rudimentary types of bootstrap interval.