stepp.CI: The constructor to create the stmodelCI object

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/stmodelCI.R

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

1
	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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
##
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 Jan. 13, 2021, 5:25 p.m.

Related to stepp.CI in stepp...