stepp.CI: The constructor to create the stmodelCI object

In stepp: Subpopulation Treatment Effect Pattern Plot (STEPP)

The constructor to create the stmodelCI object

Description

This is the constructor function for the stmodelCI object. This object sets up the data with a stepp model using competing risks method for analysis. (CI stands for Cumulative Incidence.)

The model explores the treatment-effect interactions in competing risks data arising from two or more treatment arms of a clinical trial. A permutation distribution approach to inference is implemented that permutes covariate values within a 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.CI(coltrt, coltime, coltype, trts, timePoint)

Arguments

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

Value

It returns the stmodelCI 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.

Lazar AA, Cole BF, Bonetti M, Gelber RD. Evaluation of treatment-effect heterogeneity usiing biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot. Journal of Clinical Oncology, 2010; 28(29): 4539-4544.

See Also

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

Examples

##
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 <- stepp.CI(coltrt=Txassign, trts=c(0,1), coltime=time, coltype=type, timePoint=1.0)

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