influences {CaseCohortCoxSurvival}  R Documentation 
Computes the influences on the logrelative hazard, baseline hazards at each unique event time, cumulative baseline hazard in a given time interval [Tau1, Tau2] and on the pure risk in [Tau1, Tau2] and for a given covariate profile x. Can take calibration of the design weights into account.
influences(mod, Tau1 = NULL, Tau2 = NULL, x = NULL, calibrated = NULL,
A = NULL)
mod 
a cox model object, result of function coxph. 
Tau1 
left bound of the time interval considered for the cumulative baseline hazard and pure risk. Default is the first event time. 
Tau2 
right bound of the time interval considered for the cumulative baseline hazard and pure risk. Default is the last event time. 
x 
vector of length 
calibrated 
are calibrated weights used for the estimation of the
parameters? If 
A 

influences
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.missingdata
.
influences
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.
infl.lambda0.t
: matrix with the overall influences on the baseline hazards estimates at each unique event time.
infl.Lambda0.Tau1Tau2.hat
: vector with the overall influences on the cumulative baseline hazard estimate in [Tau1, Tau2].
infl.Pi.x.Tau1Tau2.hat
: vector with the overall influences on the pure risk estimate in [Tau1, Tau2] and for covariate profile x
.
infl2.beta
: matrix with the phasetwo influences on the logrelative hazard estimates. Returned if calibrated = TRUE
.
infl2.lambda0.t
: matrix with the phasetwo influences on the baseline hazards estimates at each unique event time. Returned if calibrated = TRUE
.
infl2.Lambda0.Tau1Tau2.hat
: vector with the phasetwo influences on the cumulative baseline hazard estimate in [Tau1, Tau2]. Returned if calibrated = TRUE
.
infl2.Pi.x.Tau1Tau2.hat
: vector with the phasetwo influences on the pure risk estimate in [Tau1, Tau2] and for covariate profile x
. Returned if calibrated = TRUE
.
beta.hat
: vector of length p
with logrelative hazard estimates.
lambda0.t.hat
: vector with baseline hazards estimates at each unique event time.
Lambda0.Tau1Tau2.hat
: cumulative baseline hazard estimate in [Tau1, Tau2].
Pi.x.Tau1Tau2.hat
: pure risk estimate in [Tau1, Tau2] and for covariate profile x
.
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.RH
, 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$A # matrix with auxiliary variables values in the cohort
Tau1 < 0 # given time interval for the pure risk
Tau2 < 8
x < c(1, 1, 0.6) # given covariate profile for the pure risk
# Estimation using the whole cohort
mod.cohort < coxph(Surv(times, status) ~ X1 + X2 + X3, data = cohort,
robust = TRUE)
estimation.cohort < influences(mod.cohort, Tau1 = Tau1, Tau2 = Tau2, x = x)
# print the vector with logrelative hazard estimates
#estimation.cohort$beta.hat
# print the cumulative baseline hazard estimate
#estimation.cohort$Lambda0.Tau1Tau2.hat
# print the pure risk estimate
#estimation.cohort$Pi.x.Tau1Tau2.hat
# print the influences on the logrelative hazard estimates
#estimation.cohort$infl.beta
# print the influences on the cumulative baseline hazard estimate
#estimation.cohort$infl.Lambda0.Tau1Tau2
# print the influences on the pure risk estimate
#estimation.cohort$infl.Pi.x.Tau1Tau2
# Estimation using the stratified casecohort with weights calibrated on A
mod.calib < coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights.calib, id = id, robust = TRUE)
estimation.calib < influences(mod.calib, A = A, calibrated = TRUE,
Tau1 = Tau1, Tau2 = Tau2, x = x)
# print the influences on the logrelative hazard estimates
#estimation.calib$infl.beta
# print the influences on the cumulative baseline hazard estimate
#estimation.calib$infl.Lambda0.Tau1Tau2
# print the influences on the pure risk estimate
#estimation.calib$infl.Pi.x.Tau1Tau2
# print the phasetwo influences on the logrelative hazard estimates
#estimation.calib$infl2.beta
# print the phasetwo influences on the cumulative baseline hazard estimate
#estimation.calib$infl2.Lambda0.Tau1Tau2
# print the phasetwo influences on the pure risk estimate
#estimation.calib$infl2.Pi.x.Tau1Tau2