Abstract
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.
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