Abstract
MOTIVATION: The case-cohort design only requires covariate measurements for individuals experiencing the outcome of interest (cases) and individuals in a subcohort randomly selected from the cohort. Stratified subcohort sampling and calibration of the design weights increase efficiency of relative hazard and pure risk estimates, but require specifically adapted variance estimators. Yet, the 'robust' variance formula is often inappropriately used with stratified case-cohort data. Also, weight calibration and pure risk estimation are underused, possibly because of the lack of convenient software. IMPLEMENTATION: An influence-based method for inference of case-cohort Cox model relative hazards and pure risks is implemented in the CaseCohortCoxSurvival R package. GENERAL FEATURES: CaseCohortCoxSurvival allows estimation of parameter and variance of Cox model relative hazards and pure risks from case-cohort data. It can handle stratified subcohort sampling and calibrate the design weights. Both features are properly accounted for in the variance estimation. AVAILABILITY: CaseCohortCoxSurvival is available on the Comprehensive R Archive Network at [https://cran.r-project.org/package=CaseCohortCoxSurvival].