CaseCohortCoxSurvival-package {CaseCohortCoxSurvival} | R Documentation |

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.

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.

Lola Etievant, Mitchell H. Gail

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

[Package *CaseCohortCoxSurvival* version 0.0.32 Index]