stmodelKM-class function

Class "stmodelKM"

This is the S4 class for the stepp model of survival data using Kaplan-Meier method. 1.1

class

Objects from the Class

Objects can be created by calls of the form new("stmodelKM", ...) or by

the constructor function stmodel.KM.

Slots

• coltrt:: Object of class "numeric"

   the treatment variable
  

• survTime:: Object of class "numeric"

   the time to event variable
  

• censor:: Object of class "numeric"

   the censor variable
  

• trts:: Object of class "numeric"

   a vector containing the codes for the 2 treatment groups, first and second treatment groups, respectively
  

• timePoint:: Object of class "numeric"

   timepoint to estimate survival
  

Extends

Class "stmodel", directly.

Methods

• estimate: signature(.Object = "stmodelKM"):

   estimate the effect in absolute and relative scale of the overall population and each subpopulation.
  

• print: signature(.Object = "stmodelKM"):

   print the estimate, covariance matrices and statistics.
  

• test: signature(.Object = "stmodelKM"):

   perform the permutation tests or GEE and obtain various statistics.
  

Returns

The new method returns the stmodelKM object.

The estimate method returns a list with the following fields:

• model: the stepp model - "KMe"

• sObs1: a vector of effect estimates of all subpopulations based on the first treatment

• sSE1: a vector of standard errors of effect estimates of all subpopulations based on the first treatment

• oObs1: effect estimate of the entire population based on the first treatment

• oSE1: the standard error of the effect estimate of the entire population based on the first treatment

• sObs2: a vector of effect estimates of all subpopulations based on the group treatment

• sSE2: a vector of standard errors of effect estimates of all subpopulations based on the first treatment

• oObs2: effect estimate of the entire population based on the first treatment

• oSE2: the standard error of the effect estimate of the entire population based on the first 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 first and second treatments

• logHRSE: a vector of standard error of the log hazard ratio estimate of the subpopulations comparing first and second treatment

• ologHR: the log hazard ratio estimate of the entire population comparing first and second treatment

• ologHRSE: the standard error of the log hazard ratio estimate of the entire population comparing first and second treatment

• logHRw: Wald's statistics for the log hazard ratio between the two treatment

The test method returns a list with the following fields:

• model: the stepp model - "KMt"

• 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 ratios

• haHRsigma: the homogeneous association covariance matrix for subpopulations based on hazard ratios

• 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

Author(s)

Wai-Ki YIp

See Also

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

Examples

showClass("stmodelKM")

#GENERATE TREATMENT VARIABLE:
N <- 1000
Txassign <- sample(c(1,2), N, replace=TRUE, prob=c(1/2, 1/2))
n1 <- length(Txassign[Txassign==1])
n2 <- N - n1
#GENERATE A COVARIATE:
covariate <- rnorm(N, 55, 7)
#GENERATE SURVIVAL AND CENSORING VARIABLES ASSUMING A TREATMENT COVARIATE INTERACTION:
Entry <- sort( runif(N, 0, 5) )
SurvT1 <- .5
beta0 <-  -65 / 75
beta1 <- 2 / 75
Surv <- rep(0, N)
lambda1 <- -log(SurvT1) / 4
Surv[Txassign==1] <- rexp(n1, lambda1)
Surv[Txassign==2] <- rexp(n2, (lambda1*(beta0+beta1*covariate[Txassign==2])))
EventTimes <- rep(0, N)
EventTimes <- Entry + Surv
censor <- rep(0, N)
time <- rep(0,N)
for ( i in 1:N )
   {
     censor[i] <- ifelse( EventTimes[i] <= 7, 1, 0 )
     time[i] <- ifelse( EventTimes[i] < 7, Surv[i], 7 - Entry[i] ) 
   }

modKM <- new("stmodelKM", coltrt=Txassign, survTime=time, censor=censor, trts=c(1,2), timePoint=4)