stmodelCI-class | R Documentation |

`"stmodelCI"`

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`

```
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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