estimation.CumBH {CaseCohortCoxSurvival}  R Documentation 
Estimates the logrelative hazard, baseline hazards at each unique event time and cumulative baseline hazard in a given time interval [Tau1, Tau2].
estimation.CumBH(mod, Tau1 = NULL, Tau2 = NULL, missing.data = FALSE,
riskmat.phase2 = NULL, dNt.phase2 = NULL, status.phase2 = NULL)
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 phasetwo 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 phasetwo data.
Needs to be provided if 
status.phase2 
vector indicating the case status in the phasetwo data.
Needs to be provided if 
estimation.CumBH
returns the logrelative hazard estimates provided by
mod
, and estimates the baseline hazard point mass at any event time
nonparametrically.
estimation.CumBH
works for estimation from a casecohort with design weights
or calibrated weights, when the casecohort consists of the subcohort and cases
not in the subcohort (i.e., casecohort obtained from two phases of sampling),
as well as with design weights when covariate data was missing for certain
individuals in the phasetwo data (i.e., casecohort obtained from three phases
of sampling).
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].
Breslow, N. (1974). Covariance Analysis of Censored Survival Data. Biometrics, 30, 8999.
Etievant, L., Gail, M.H. (2023). Cox model inference for relative hazard and pure risk from stratified weightcalibrated casecohort data. Submitted.
estimation
, estimation.PR
, influences
, influences.RH
,
influences.CumBH
, influences.PR
,
influences.missingdata
, influences.RH.missingdata
, influences.CumBH.missingdata
,
and influences.PR.missingdata
data(dataexample.missingdata, package="CaseCohortCoxSurvival")
cohort < dataexample.missingdata$cohort # a simulated cohort
casecohort < dataexample.missingdata$casecohort # a simulated stratified casecohort
riskmat.phase2 < dataexample.missingdata$riskmat.phase2
dNt.phase2 < dataexample.missingdata$dNt.phase2
# Estimation using the whole cohort
mod.cohort < coxph(Surv(times, status) ~ X1 + X2 + X3, data = cohort,
robust = TRUE)
estimation.cohort < estimation.CumBH(mod = mod.cohort, Tau1 = 0, Tau2 = 8)
# print the vector with logrelative hazard estimates
estimation.cohort$beta.hat
# print the cumulative baseline hazard estimate
estimation.cohort$Lambda0.Tau1Tau2.hat
# Estimation using the stratified casecohort with known design weights
mod < coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights.true, id = id, robust = TRUE)
estimation.casecohort < estimation.CumBH(mod = mod, Tau1 = 0, Tau2 = 8,
missing.data = TRUE,
riskmat.phase2 = riskmat.phase2,
dNt.phase2 = dNt.phase2)
# print the vector with logrelative hazard estimates
estimation.casecohort$beta.hat
# print the cumulative baseline hazard estimate
estimation.casecohort$Lambda0.Tau1Tau2.hat