Barlow W E, Ichikawa L, Rosner D, Izumi S
Center for Health Studies, Group Health Cooperative, Seattle, Washington 98101-1448, USA.
J Clin Epidemiol. 1999 Dec;52(12):1165-72. doi: 10.1016/s0895-4356(99)00102-x.
The case-cohort design is most useful in analyzing time to failure in a large cohort in which failure is rare. Covariate information is collected from all failures and a representative sample of censored observations. Sampling is done without respect to time or disease status, and, therefore, the design is more flexible than a nested case-control design. Despite the efficiency of the methods, case-cohort designs are not often used because of perceived analytic complexity. In this article, we illustrate computation of a simple variance estimator and discuss model fitting techniques in SAS. Three different weighting methods are considered. Model fitting is demonstrated in an occupational exposure study of nickel refinery workers. The design is compared to a nested case-control design with respect to analysis and efficiency in a small simulation. In this example, case-cohort sampling from the full cohort was more efficient than using a comparable nested case-control design.
病例队列设计在分析大型队列中罕见失败事件发生时间时最为有用。协变量信息从所有失败事件以及经过审查的观察对象的代表性样本中收集。抽样过程不考虑时间或疾病状态，因此，该设计比巢式病例对照设计更灵活。尽管这些方法具有效率，但由于人们认为其分析复杂，病例队列设计并不常用。在本文中，我们展示了一个简单方差估计量的计算，并讨论了SAS中的模型拟合技术。我们考虑了三种不同的加权方法。在一项镍精炼厂工人职业暴露研究中展示了模型拟合。在一个小型模拟中，就分析和效率而言，将该设计与巢式病例对照设计进行了比较。在这个例子中，从整个队列中进行病例队列抽样比使用类似的巢式病例对照设计更有效。
