influences.RH {CaseCohortCoxSurvival}

influences.RH

Description

Computes the influences on the log-relative hazard. Can take calibration of the design weights into account.

Usage

influences.RH(mod, calibrated = NULL, A = NULL)

Arguments

mod

a cox model object, result of function coxph.

calibrated

are calibrated weights used for the estimation of the parameters? If calibrated = TRUE, the argument below needs to be provided. Default is FALSE.

A

n \times q matrix with the values of the auxiliary variables used for the calibration of the weights in the whole cohort. Needs to be provided if calibrated = TRUE.

Details

influences.RH works for estimation from a case-cohort with design weights or calibrated weights (case-cohort consisting of the subcohort and cases not in the subcohort, i.e., case-cohort obtained from two phases of sampling).

If covariate information is missing for certain individuals in the phase-two data (i.e., case-cohort obtained from three phases of sampling), use influences.RH.missingdata.

influence.RH uses the influence formulas provided in Etievant and Gail (2023). More precisely, as in Section 3.2 if calibrated = FALSE, and as in Section 4.3 if calibrated = TRUE.

If calibrated = FALSE, the infuences are only provided for the individuals in the case-cohort. If calibrated = TRUE, the influences are provided for all the individuals in the cohort.

Value

infl.beta: matrix with the overall influences on the log-relative hazard estimates.

infl2.beta: matrix with the phase-two influences on the log-relative hazard estimates. Returned if calibrated = TRUE.

beta.hat: vector of length p with log-relative hazard estimates.

References

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

See Also

estimation, estimation.CumBH, estimation.PR, influences, influences.CumBH, influences.PR, influences.missingdata, influences.RH.missingdata, influences.CumBH.missingdata,
influences.PR.missingdata, robustvariance and variance.

Examples

data(dataexample, package="CaseCohortCoxSurvival")

cohort      <- dataexample$cohort # a simulated cohort
casecohort  <- dataexample$casecohort # a simulated stratified case-cohort
A           <- dataexample$A # matrix with auxiliary variables values in the cohort


# Estimation using the whole cohort

mod.cohort <- coxph(Surv(times, status) ~ X1 + X2 + X3, data = cohort, 
                    robust = TRUE)
estimation.cohort <- influences.RH(mod.cohort)

# print the vector with log-relative hazard estimates
#estimation.cohort$beta.hat

# print the influences on the log-relative hazard estimates
#estimation.cohort$infl.beta

# Estimation using the stratified case-cohort with weights calibrated on A 

mod.calib <- coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort, 
                   weight = weights.calib, id = id, robust = TRUE)
estimation.calib    <- influences.RH(mod.calib, A = A, calibrated = TRUE)

# print the influences on the log-relative hazard estimates
#estimation.calib$infl.beta

# print the phase-two influences on the log-relative hazard estimates
#estimation.calib$infl2.beta