stmodelCI-class: Class '"stmodelCI"'

In stepp: Subpopulation Treatment Effect Pattern Plot (STEPP)

Class "stmodelCI"

Description

This is the stepp model of survival data with competing risks.

Value

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 from the Class

Objects can be created by calls of the form new("stmodelCI", ...) or by
the constructor function stepp.CI.

Slots

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

Extends

Class "stmodel", directly.

Methods

estimate

signature(.Object = "stmodelCI"):
estimate the effect in absolute and relative scale of the overall and each subpopulation

print

signature(.Object = "stmodelCI"):
print the estimate, covariance matrices and statistics

test

signature(.Object = "stmodelCI"):
perform the permutation tests or GEE and obtain various statistics

Author(s)

Wai-Ki Yip

See Also

stwin, stsubpop, stmodelKM, stmodelCI, stmodelGLM, steppes, stmodel, stepp.win, stepp.subpop, stepp.KM, stepp.GLM, stepp.test, estimate, generate

Examples

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)

stepp documentation built on June 18, 2022, 5:06 p.m.