ROBUST VARIANCE-ESTIMATION FOR THE CASE-COHORT DESIGN
ROBUST VARIANCE-ESTIMATION FOR THE CASE-COHORT DESIGN
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DOI：
10.2307/2533444
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发表时间：
1994-12-01
期刊：
影响因子：
1.9
通讯作者：
BARLOW, WE
中科院分区：
文献类型：
--
作者：
BARLOW, WE
罕见结果的大型队列研究需要广泛的数据收集，通常是针对许多相对缺乏信息的受试者。已经提出了对某些群体进行过度抽样的抽样方案。例如，普伦蒂斯（1986，生物统计学73，1-11）的病例-队列设计提供了一种分析失效时间数据的有效方法。然而，方差估计必须明确校正相关的分数贡献。提出了一种简单的鲁棒方差估计器，允许更复杂的采样机制。方差估计值使用了个体影响函数方差的折刀估计值，并被证明与Lin和Wei（1989，Journal of the American Statistical Association 84，1074-1078）为标准考克斯模型提出的稳健方差估计值等效。模拟结果表明，与校正的渐近估计和适当的测试大小的协议。该技术说明了数据评估乳腺X线摄影筛查在降低乳腺癌死亡率的疗效。
Large cohort studies of rare outcomes require extensive data collection, often for many relatively uninformative subjects. Sampling schemes have been proposed that oversample certain groups. For example, the case-cohort design of Prentice (1986, Biometrika 73, 1-11) provides an efficient method of analysis of failure time data. However, the variance estimate must explicitly correct for correlated score contributions. A simple robust variance estimator is proposed that allows for more complicated sampling mechanisms. The variance estimate uses a jackknife estimate of the variance of the individual influence function and is shown to be equivalent to a robust variance estimator proposed by Lin and Wei (1989, Journal of the American Statistical Association 84, 1074-1078) for the standard Cox model. Simulation results indicate excellent agreement with corrected asymptotic estimates and appropriate test size. The technique is illustrated with data evaluating the efficacy of mammography screening in reducing breast cancer mortality.