stepp.KM: The constructor to create the stmodelKM object

In stepp: Subpopulation Treatment Effect Pattern Plot (STEPP)

The constructor to create the stmodelKM object

Description

This is the constructor function for the stmodelKM object. This object sets up the data with a stepp model using the Kaplan-Meier method for analysis.

The model explores the treatment-covariate interactions in survival data arising from two treatment arms of a clinical trial. The treatment effects are measured using survival functions at a specified time point estimated from the Kaplan-Meier method and the hazard ratio based on observed-minus-expected estimation. A permutation distribution approach to inference is implemented, based on permuting the covariate values within each treatment group. The statistical significance of observed heterogeneity of treatment effects is calculated using permutation tests:

1) for the maximum difference between each subpopulation effect and the overall population treatment effect or supremum based test statistic;
2) for the difference between each subpopulation effect and the overall population treatment effect, which resembles the chi-square statistic.

Usage

	stepp.KM(coltrt, survTime, censor, trts, timePoint)

Arguments

coltrt	the treatment variable
survTime	the time to event variable
censor	the censor variable
trts	a vector containing the codes for the 2 treatment arms, 1st and 2nd treatment groups, respectively
timePoint	timepoint to estimate survival

Value

It returns the stmodelKM object.

Author(s)

Wai-Ki Yip

References

Bonetti M, Gelber RD. Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics 2004; 5(3):465-481.

Bonetti M, Zahrieh D, Cole BF, Gelber RD. A small sample study of the STEPP approach to assessing treatment-covariate interactions in survival data. Statistics in Medicine 2009; 28(8):1255-68.

See Also

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

Examples

#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 <- stepp.KM( coltrt=Txassign, survTime=time, censor=censor, trts=c(1,2), timePoint=4)

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