  The case-cohort design allows analysis of multiple endpoints and only
requires covariates to be measured for cases and non-cases in a random
subcohort from the cohort. Stratification of subcohort sampling and weight
calibration increase efficiency of estimates of log-relative hazards and
covariate-specific pure risk, but they may require specifically adapted
variance estimators. Some recent articles in epidemiology and medical journals
used an inappropriate "robust" variance estimator. In addition, stratification,
weight calibration and analysis of pure risk seem underutilized in case-cohort
studies, possibly because practitioners are put off by the varied technical
methodologic literature and lack of convenient software. We recently proposed a
unified approach to variance estimation for Cox model log-relative hazards and
pure risks, and we implemented it in an R package, CaseCohortCoxSurvival,
available on CRAN, that allows appropriate and convenient analysis of
case-cohort data, with and without stratification, weight calibration, or
missing at random phase-two data. Here we illustrate how easy it is to use
CaseCohortCoxSurvival to analyze data from the Prostate, Lung, Colorectal and
Ovarian Cancer Screening Trial to estimate pure covariate-specific risk of
prostate cancer incidence with various case-cohort design and analysis options.
These analyses also indicate situations where the simple "robust" variance is
too large.
