estimation {CaseCohortCoxSurvival}  R Documentation 
estimation
Description
Estimates the logrelative hazard, baseline hazards at each unique event time, cumulative baseline hazard in a given time interval [Tau1, Tau2] and pure risk in [Tau1, Tau2] and for a given covariate profile x.
Usage
estimation(mod, Tau1 = NULL, Tau2 = NULL, x = NULL, missing.data = NULL,
riskmat.phase2 = NULL, dNt.phase2 = NULL, status.phase2 = NULL)
Arguments
mod 
a Cox model object, result of function 
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 
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 
Details
estimation
returns the logrelative hazard estimates provided by
mod
, and estimates the baseline hazard point mass at any event time
nonparametrically.
estimation
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).
Value
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
.
References
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.
See Also
estimation.CumBH
, 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, 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
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 < estimation(mod = 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$Pi.x.Tau1Tau2.hat
# Estimation using the stratified casecohort with known design weights
mod < coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights.true, id = id, robust = TRUE)
estimation.casecohort < estimation(mod = mod, Tau1 = Tau1, Tau2 = Tau2, x = x,
missing.data = TRUE,
riskmat.phase2 = riskmat.phase2,
dNt.phase2 = dNt.phase2)
# print the vector with logrelative hazard estimates
estimation.casecohort$beta.hat
# print the cumulative baseline hazard estimate
estimation.casecohort$Lambda0.Tau1Tau2.hat
# print the pure risk estimate
estimation.casecohort$Pi.x.Tau1Tau2.hat