influences.PR {CaseCohortCoxSurvival} | R Documentation |
influences.PR
Description
Computes the influences on the pure risk in the time interval [Tau1, Tau2] and for a given covariate profile x, from that on the log-relative hazard and cumulative baseline hazard. Can take calibration of the design weights into account.
Usage
influences.PR(beta, Lambda0.Tau1Tau2, x = NULL, infl.beta,
infl.Lambda0.Tau1Tau2, calibrated = NULL, infl2.beta = NULL,
infl2.Lambda0.Tau1Tau2 = NULL)
Arguments
beta |
vector of length |
Lambda0.Tau1Tau2 |
cumulative baseline hazard in [Tau1, Tau2]. |
x |
vector of length |
infl.beta |
matrix with the overall influences on the log-relative hazard estimates. |
infl.Lambda0.Tau1Tau2 |
vector with the overall influences on the cumulative baseline hazard estimate in [Tau1, Tau2]. |
calibrated |
are calibrated weights used for the estimation of the
parameters? If |
infl2.beta |
matrix with the phase-two influences on the log-relative
hazard estimates. Needs to be provided if |
infl2.Lambda0.Tau1Tau2 |
vector with the phase-two influences on the
cumulative baseline hazard estimate in [Tau1, Tau2]. Needs to be provided
if |
Details
influences.PR
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.PR.missingdata
.
influences
uses the influence formulas provided in Etievant and Gail
(2024).
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.Pi.x.Tau1Tau2.hat
: vector with the overall influences on the pure risk estimate in [Tau1, Tau2] and for covariate profile x
.
infl2.Pi.x.Tau1Tau2.hat
: vector with the phase-two influences on the pure risk estimate in [Tau1, Tau2] and for covariate profile x
.
Returned if calibrated = TRUE
.
Pi.x.Tau1Tau2.hat
: pure risk estimate in [Tau1, Tau2] and for covariate profile x
.
References
Etievant, L., Gail, M. H. (2024). Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data. Lifetime Data Analysis, 30, 572-599.
See Also
estimation
, estimation.CumBH
, estimation.PR
,
influences
, influences.RH
, influences.CumBH
,
influences.missingdata
, influences.RH.missingdata
,
influences.CumBH.missingdata
, influences.PR.missingdata
,
robustvariance
and variance
.
Examples
data(dataexample.stratified, package="CaseCohortCoxSurvival")
cohort <- dataexample.stratified$cohort
casecohort <- cohort[which(cohort$status == 1 |
cohort$subcohort == 1),] # the stratified case-cohort
casecohort$weights <- casecohort$strata.n / casecohort$strata.m
casecohort$weights[which(casecohort$status == 1)] <- 1
Tau1 <- 0
Tau2 <- 8
x <- c(-1, 1, -0.6) # given covariate profile for the pure risk
# Estimation using the stratified case cohort with design weights
mod <- coxph(Surv(event.time, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights, id = id, robust = TRUE)
est <- influences(mod, Tau1 = Tau1, Tau2 = Tau2, x = x)
# print the influences on the pure risk estimate
# est$infl.Pi.x.Tau1Tau2