stmodelCI-class | R Documentation |
"stmodelCI"
This is the stepp model of survival data with competing risks.
The new method returns the stmodelCI object.
The estimate method returns a list with the following fields:
model |
the stepp model - "CIe" |
sObs1 |
a vector of effect estimates of all subpopulations based on the 1st treatment |
sSE1 |
a vector of standard errors of effect estimates of all subpopulations based on the 1st treatment |
oObs1 |
effect estimate of the entire population based on the 1st treatment |
oSE1 |
the standard error of the effect estimate of the entire population based on the 1st treatment |
sObs2 |
a vector of effect estimates of all subpopulations based on the 1st treatment |
sSE2 |
a vector of standard errors of effect estimates of all subpopulations based on the 1st treatment |
oObs2 |
effect estimate of the entire population based on the 1st treatment |
oSE2 |
the standard error of the effect estimate of the entire population based on the 1st treatment |
skmw |
Wald's statistics for the effect estimate differences between the two treatments |
logHR |
a vector of log hazard ratio estimate of the subpopulations comparing 1st and 2nd treatments |
logHRSE |
a vector of standard error of the log hazard ratio estimate of the subpopulations comparing 1st and 2nd treatments |
ologHR |
the log hazard ratio estimate of the entire population comparing 1st and 2nd treatments |
ologHRSE |
the standard error of the log hazard ratio estimate of the entire population comparing 1st and 2nd treatments |
logHRw |
Wald's statistics for the log hazard ratio between the two treatments |
The test method returns a list with the following fields:
model |
the stepp model - "CIt" |
sigma |
the covariance matrix for subpopulations based on effect differences |
hasigma |
the homogeneous association covariance matrix for subpopulations based on effect differences |
HRsigma |
the covariance matrix for the subpopulations based on hazard ratio |
haHRsigma |
the homogeneous association covariance matrix for subpopulations based on hazard ratio |
pvalue |
the supremum pvalue based on effect difference |
chi2pvalue |
the chisquare pvalue based on effect difference |
hapvalue |
the homogeneous association pvalue based on effect difference |
HRpvalue |
the supremum pvalue based on hazard ratio |
haHRpvalue |
the homogeneous association pvalue based on hazard ratio |
Objects can be created by calls of the form new("stmodelCI", ...)
or by
the constructor function stepp.CI.
coltrt
:Object of class "numeric"
the treatment variable
coltime
:Object of class "numeric"
the time to event variable
coltype
:Object of class "numeric"
variable with distinct codes for different causes of failure where coltype=0 for censored observations; coltype=1 for event of interest; coltype=2 for other causes of failure
trts
:Object of class "numeric"
a vector containing the codes for the 2 treatment groups, 1st and 2nd treatment groups, respectively
timePoint
:Object of class "numeric"
timepoint to estimate survival
Class "stmodel"
, directly.
signature(.Object = "stmodelCI")
:
estimate the effect in absolute and relative scale of the overall and each subpopulation
signature(.Object = "stmodelCI")
:
print the estimate, covariance matrices and statistics
signature(.Object = "stmodelCI")
:
perform the permutation tests or GEE and obtain various statistics
Wai-Ki Yip
stwin
, stsubpop
, stmodelKM
,
stmodelCI
, stmodelGLM
,
steppes
, stmodel
,
stepp.win
, stepp.subpop
, stepp.KM
,
stepp.GLM
,
stepp.test
, estimate
, generate
showClass("stmodelCI")
##
n <- 1000 # set the sample size
mu <- 0 # set the mean and sd of the covariate
sigma <- 1
beta0 <- log(-log(0.5)) # set the intercept for the log hazard
beta1 <- -0.2 # set the slope on the covariate
beta2 <- 0.5 # set the slope on the treatment indicator
beta3 <- 0.7 # set the slope on the interaction
prob2 <- 0.2 # set the proportion type 2 events
cprob <- 0.3 # set the proportion censored
set.seed(7775432) # set the random number seed
covariate <- rnorm(n,mean=mu,sd=sigma) # generate the covariate values
Txassign <- rbinom(n,1,0.5) # generate the treatment indicator
x3 <- covariate*Txassign # compute interaction term
# compute the hazard for type 1 event
lambda1 <- exp(beta0+beta1*covariate+beta2*Txassign+beta3*x3)
lambda2 <- prob2*lambda1/(1-prob2) # compute the hazard for the type 2 event
# compute the hazard for censoring time
lambda0 <- cprob*(lambda1+lambda2)/(1-cprob)
t1 <- rexp(n,rate=lambda1) # generate the survival time for type 1 event
t2 <- rexp(n,rate=lambda2) # generate the survival time for type 2 event
t0 <- rexp(n,rate=lambda0) # generate the censoring time
time <- pmin(t0,t1,t2) # compute the observed survival time
type <- rep(0,n)
type[(t1 < t0)&(t1 < t2)] <- 1
type[(t2 < t0)&(t2 < t1)] <- 2
# create the stepp model object to analyze the data using Cumulative Incidence approach
x <- new ("stmodelCI", coltrt=Txassign, trts=c(0,1), coltime=time, coltype=type, timePoint=1.0)
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