Description Value Objects from the Class Slots Extends Methods Author(s) See Also Examples

This is the stepp model of survival data with competing risks.

The new method returns the stmodelCI object.

The estimate method returns a list with the following fields:

`model` |
the stepp model - "CIe" |

`sObs1` |
a vector of effect estimates of all subpopulations based on the 1st treatment |

`sSE1` |
a vector of standard errors of effect estimates of all subpopulations based on the 1st treatment |

`oObs1` |
effect estimate of the entire population based on the 1st treatment |

`oSE1` |
the standard error of the effect estimate of the entire population based on the 1st treatment |

`sObs2` |
a vector of effect estimates of all subpopulations based on the 1st treatment |

`sSE2` |
a vector of standard errors of effect estimates of all subpopulations based on the 1st treatment |

`oObs2` |
effect estimate of the entire population based on the 1st treatment |

`oSE2` |
the standard error of the effect estimate of the entire population based on the 1st 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 1st and 2nd treatments |

`logHRSE` |
a vector of standard error of the log hazard ratio estimate of the subpopulations comparing 1st and 2nd treatments |

`ologHR` |
the log hazard ratio estimate of the entire population comparing 1st and 2nd treatments |

`ologHRSE` |
the standard error of the log hazard ratio estimate of the entire population comparing 1st and 2nd treatments |

`logHRw` |
Wald's statistics for the log hazard ratio between the two treatments |

The test method returns a list with the following fields:

`model` |
the stepp model - "CIt" |

`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 ratio |

`haHRsigma` |
the homogeneous association covariance matrix for subpopulations based on hazard ratio |

`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 |

`HRpvalue` |
the supremum pvalue based on hazard ratio |

`haHRpvalue` |
the homogeneous association pvalue based on hazard ratio |

Objects can be created by calls of the form `new("stmodelCI", ...)`

or by

the constructor function stepp.CI.

`coltrt`

:Object of class

`"numeric"`

the treatment variable`coltime`

:Object of class

`"numeric"`

the time to event variable`coltype`

:Object of class

`"numeric"`

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`

:Object of class

`"numeric"`

a vector containing the codes for the 2 treatment groups, 1st and 2nd treatment groups, respectively`timePoint`

:Object of class

`"numeric"`

timepoint to estimate survival

Class `"stmodel"`

, directly.

- estimate
`signature(.Object = "stmodelCI")`

:

estimate the effect in absolute and relative scale of the overall and each subpopulation`signature(.Object = "stmodelCI")`

:

print the estimate, covariance matrices and statistics- test
`signature(.Object = "stmodelCI")`

:

perform the permutation tests or GEE and obtain various statistics

Wai-Ki Yip

`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 33 34 | ```
showClass("stmodelCI")
##
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 <- new ("stmodelCI", coltrt=Txassign, trts=c(0,1), coltime=time, coltype=type, timePoint=1.0)
``` |

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