  A case-cohort design is a two-phase sampling design frequently used to
analyze censored survival data in a cost-effective way, where a subcohort is
usually selected using simple random sampling or stratified simple random
sampling. In this paper, we propose an efficient sampling procedure based on
balanced sampling when selecting a subcohort in a case-cohort design. A sample
selected via a balanced sampling procedure automatically calibrates auxiliary
variables. When fitting a Cox model, calibrating sampling weights has been
shown to lead to more efficient estimators of the regression coefficients
(Breslow et al., 2009a, b). The reduced variabilities over its counterpart with
a simple random sampling are shown via extensive simulation experiments. The
proposed design and estimation procedure are also illustrated with the
well-known National Wilms Tumor Study dataset.
