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Lola Etiévant, Mitchell H Gail, Software Application Profile: CaseCohortCoxSurvival—an R package for case-cohort inference for relative hazard and pure risk under the Cox model, International Journal of Epidemiology, Volume 54, Issue 2, April 2025, dyaf016, https://doi.org/10.1093/ije/dyaf016
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Abstract
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.
An influence-based method for inference of case-cohort Cox model relative hazards and pure risks is implemented in the CaseCohortCoxSurvival R package.
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.
CaseCohortCoxSurvival is available on the Comprehensive R Archive Network at [https://cran.r-project.org/package=CaseCohortCoxSurvival].