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

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.

1 | ```
stepp.CI(coltrt, coltime, coltype, trts, timePoint)
``` |

`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 |

It returns the stmodelCI object.

Wai-Ki Yip

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.

`stwin`

, `stsubpop`

, `stmodelKM`

,
`stmodelCI`

, `stmodelGLM`

,
`steppes`

, `stmodel`

,
`stepp.win`

, `stepp.subpop`

, `stepp.KM`

,
`stepp.GLM`

,
`stepp.test`

, `estimate`

, `generate`

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)
``` |

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