CaseCohortCoxSurvival: an R Package for Case-Cohort Inference for Relative Hazard and Pure Risk under the Cox Model

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.