estimation.CumBH {CaseCohortCoxSurvival} | R Documentation |
estimation.CumBH
Description
Estimates the log-relative hazard, baseline hazards at each unique event time and cumulative baseline hazard in a given time interval [Tau1, Tau2].
Usage
estimation.CumBH(mod, Tau1 = NULL, Tau2 = NULL, missing.data = FALSE,
riskmat.phase2 = NULL, dNt.phase2 = NULL, status.phase2 = NULL)
Arguments
mod |
a Cox model object, result of function coxph. |
Tau1 |
left bound of the time interval considered for the cumulative baseline hazard. Default is the first event time. |
Tau2 |
right bound of the time interval considered for the cumulative baseline hazard. Default is the last event time. |
missing.data |
was data on the |
riskmat.phase2 |
at risk matrix for the phase-two data at all of the case
event times, even those with missing covariate data. Needs to be provided if
|
dNt.phase2 |
counting process matrix for failures in the phase-two data.
Needs to be provided if |
status.phase2 |
vector indicating the case status in the phase-two data.
Needs to be provided if |
Details
estimation.CumBH
returns the log-relative hazard estimates provided by
mod
, and estimates the baseline hazard point mass at any event time
non-parametrically.
estimation.CumBH
works for estimation from a case-cohort with design weights
or calibrated weights, when the case-cohort consists of the subcohort and cases
not in the subcohort (i.e., case-cohort obtained from two phases of sampling),
as well as with design weights when covariate data was missing for certain
individuals in the phase-two data (i.e., case-cohort obtained from three phases
of sampling).
Value
beta.hat
: vector of length p
with log-relative 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].
References
Breslow, N. (1974). Covariance Analysis of Censored Survival Data. Biometrics, 30, 89-99.
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.PR
, influences
, influences.RH
,
influences.CumBH
, influences.PR
,
influences.missingdata
, influences.RH.missingdata
, influences.CumBH.missingdata
,
and influences.PR.missingdata
Examples
data(dataexample.missingdata.stratified, package="CaseCohortCoxSurvival")
cohort <- dataexample.missingdata.stratified$cohort
phase2 <- cohort[which(cohort$phase2 == 1),] # the phase-two sample
casecohort <- cohort[which(cohort$phase3 == 1),] # the stratified case-cohort
B.phase2 <- cbind(1 * (phase2$W3 == 0), 1 * (phase2$W3 == 1))
rownames(B.phase2) <- cohort[cohort$phase2 == 1, "id"]
B.phase3 <- cbind(1 * (casecohort$W3 == 0), 1 * (casecohort$W3 == 1))
rownames(B.phase3) <- cohort[cohort$phase3 == 1, "id"]
total.B.phase2 <- colSums(B.phase2)
J3 <- ncol(B.phase3)
n <- nrow(cohort)
# Quantities needed for estimation of the cumulative baseline hazard when
# covariate data is missing
mod.cohort <- coxph(Surv(event.time, status) ~ X2, data = cohort,
robust = TRUE) # X2 is available on all cohort members
mod.cohort.detail <- coxph.detail(mod.cohort, riskmat = TRUE)
riskmat.phase2 <- with(cohort, mod.cohort.detail$riskmat[phase2 == 1,])
rownames(riskmat.phase2) <- cohort[cohort$phase2 == 1, "id"]
observed.times.phase2 <- apply(riskmat.phase2, 1,
function(v) {which.max(cumsum(v))})
dNt.phase2 <- matrix(0, nrow(riskmat.phase2), ncol(riskmat.phase2))
dNt.phase2[cbind(1:nrow(riskmat.phase2), observed.times.phase2)] <- 1
dNt.phase2 <- sweep(dNt.phase2, 1, phase2$status, "*")
colnames(dNt.phase2) <- colnames(riskmat.phase2)
rownames(dNt.phase2) <- rownames(riskmat.phase2)
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 stratified case cohort with true known design weights
mod.true <- coxph(Surv(event.time, status) ~ X1 + X2 + X3, data = casecohort,
weight = weight.true, id = id, robust = TRUE)
est.true <- estimation(mod.true, Tau1 = Tau1, Tau2 = Tau2, x = x,
missing.data = TRUE,
riskmat.phase2 = riskmat.phase2,
dNt.phase2 = dNt.phase2)
est.true <- estimation.CumBH(mod.true, Tau1 = Tau1, Tau2 = Tau2,
missing.data = TRUE,
riskmat.phase2 = riskmat.phase2,
dNt.phase2 = dNt.phase2)
# print the cumulative baseline hazard estimate
est.true$Lambda0.Tau1Tau2.hat