influences.RH {CaseCohortCoxSurvival} | R Documentation |
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 |
A |
|
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
(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.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. (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.CumBH
, influences.PR
,
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 log-relative hazard estimates
# est$infl.beta