CaseCohortCoxSurvival-package {CaseCohortCoxSurvival}

Case-Cohort Cox Survival Inference

Description

This package uses case-cohort data to estimate log-relative hazard, baseline hazards at each unique event time, cumulative baseline hazard in a given time interval and pure risk on the time interval and for a given covariate profile, under the Cox model. For the corresponding variance estimation, it relies on influence functions and follows the complete variance decomposition, to enable correct analysis of case-cohort data with and without stratification, weight calibration or missing phase-two covariate data.

Details

The package provides functions implementing the methods described in Etievant and Gail (2023). More precisely, it includes

• a main driver function, caseCohortCoxSurvival.

• one function, estimatePureRisk, to estimate pure risks and the corresponding variances with additional covariate profiles.

• three functions, estimation, estimation.CumBH and estimation.PR, for parameters estimation.

• four functions, influences, influences.RH, influences.CumBH and influences.PR, for influence functions derivation when estimation is with design or calibrated weights and from a case-cohort consisting of the subcohort and cases not in the subcohort (i.e., case-cohort obtained from two phases of sampling).

• four functions, influences.missingdata, influences.RH.missingdata, influences.CumBH.missingdata and influences.PR.missingdata, for influence functions derivation when estimation is with design weights and from a case-cohort when covariate information was missing for certain individuals in the phase-two data (i.e., case-cohort obtained from three phases of sampling).

• two functions, variance and variance.missingdata, for variance estimation following complete variance decomposition (with design or calibrated weights and without missing phase-two data, or with design weights and missing phase-two covariate data).

• one function, robustvariance, for robust variance estimation.

• one function, auxiliary.construction, to compute the auxiliary variables proposed by Breslow et al. (Stat. Biosci., 2009), Breslow and Lumley (IMS, 2013), and Shin et al. (Biometrics, 2020),.

• one function, calibration, for weight calibration.

• one function, estimation.weights.phase3, for estimating the phase-three weights.

Author(s)

Lola Etievant, Mitchell H. Gail

References

Etievant, L., Gail, M.H. (2023). Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data. Submitted.