influences.RH {CaseCohortCoxSurvival}  R Documentation 
Computes the influences on the logrelative hazard. Can take calibration of the design weights into account.
influences.RH(mod, calibrated = NULL, A = NULL)
mod 
a cox model object, result of function coxph. 
calibrated 
are calibrated weights used for the estimation of the
parameters? If 
A 

influences.RH
works for estimation from a casecohort with design weights
or calibrated weights (casecohort consisting of the subcohort and cases not in
the subcohort, i.e., casecohort obtained from two phases of sampling).
If covariate information is missing for certain individuals in the phasetwo data
(i.e., casecohort 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 casecohort. If calibrated = TRUE
, the influences are provided for
all the individuals in the cohort.
infl.beta
: matrix with the overall influences on the logrelative hazard estimates.
infl2.beta
: matrix with the phasetwo influences on the logrelative hazard estimates. Returned if calibrated = TRUE
.
beta.hat
: vector of length p
with logrelative hazard estimates.
Etievant, L., Gail, M.H. (2023). Cox model inference for relative hazard and pure risk from stratified weightcalibrated casecohort data. Submitted.
estimation
, estimation.CumBH
, estimation.PR
,
influences
, influences.CumBH
, influences.PR
,
influences.missingdata
, influences.RH.missingdata
,
influences.CumBH.missingdata
,
influences.PR.missingdata
, robustvariance
and variance
.
data(dataexample, package="CaseCohortCoxSurvival")
cohort < dataexample$cohort # a simulated cohort
casecohort < dataexample$casecohort # a simulated stratified casecohort
A < dataexample$A # matrix with auxiliary variables values in the cohort
# Estimation using the whole cohort
mod.cohort < coxph(Surv(times, status) ~ X1 + X2 + X3, data = cohort,
robust = TRUE)
estimation.cohort < influences.RH(mod.cohort)
# print the vector with logrelative hazard estimates
#estimation.cohort$beta.hat
# print the influences on the logrelative hazard estimates
#estimation.cohort$infl.beta
# Estimation using the stratified casecohort with weights calibrated on A
mod.calib < coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights.calib, id = id, robust = TRUE)
estimation.calib < influences.RH(mod.calib, A = A, calibrated = TRUE)
# print the influences on the logrelative hazard estimates
#estimation.calib$infl.beta
# print the phasetwo influences on the logrelative hazard estimates
#estimation.calib$infl2.beta