stmodelKM-class | R Documentation |

`"stmodelKM"`

This is the S4 class for the stepp model of survival data using Kaplan-Meier method.

The new method returns the stmodelKM object.

The estimate method returns a list with the following fields:

`model` |
the stepp model - "KMe" |

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

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

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

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

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

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

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

`oSE2` |
the standard error of the effect estimate of the entire population based on the first 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 first and second treatments |

`logHRSE` |
a vector of standard error of the log hazard ratio estimate of the subpopulations comparing first and second treatment |

`ologHR` |
the log hazard ratio estimate of the entire population comparing first and second treatment |

`ologHRSE` |
the standard error of the log hazard ratio estimate of the entire population comparing first and second treatment |

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

The test method returns a list with the following fields:

`model` |
the stepp model - "KMt" |

`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 ratios |

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

`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 |

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

or by

the constructor function stmodel.KM.

`coltrt`

:Object of class

`"numeric"`

the treatment variable`survTime`

:Object of class

`"numeric"`

the time to event variable`censor`

:Object of class

`"numeric"`

the censor variable`trts`

:Object of class

`"numeric"`

a vector containing the codes for the 2 treatment groups, first and second treatment groups, respectively`timePoint`

:Object of class

`"numeric"`

timepoint to estimate survival

Class `"stmodel"`

, directly.

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

:

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

:

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

:

perform the permutation tests or GEE and obtain various statistics.

Wai-Ki YIp

`stwin`

, `stsubpop`

,
`stmodelCI`

, `stmodelGLM`

,
`steppes`

, `stmodel`

,
`stepp.win`

, `stepp.subpop`

, `stepp.KM`

,
`stepp.CI`

, `stepp.GLM`

,
`stepp.test`

, `estimate`

, `generate`

```
showClass("stmodelKM")
#GENERATE TREATMENT VARIABLE:
N <- 1000
Txassign <- sample(c(1,2), N, replace=TRUE, prob=c(1/2, 1/2))
n1 <- length(Txassign[Txassign==1])
n2 <- N - n1
#GENERATE A COVARIATE:
covariate <- rnorm(N, 55, 7)
#GENERATE SURVIVAL AND CENSORING VARIABLES ASSUMING A TREATMENT COVARIATE INTERACTION:
Entry <- sort( runif(N, 0, 5) )
SurvT1 <- .5
beta0 <- -65 / 75
beta1 <- 2 / 75
Surv <- rep(0, N)
lambda1 <- -log(SurvT1) / 4
Surv[Txassign==1] <- rexp(n1, lambda1)
Surv[Txassign==2] <- rexp(n2, (lambda1*(beta0+beta1*covariate[Txassign==2])))
EventTimes <- rep(0, N)
EventTimes <- Entry + Surv
censor <- rep(0, N)
time <- rep(0,N)
for ( i in 1:N )
{
censor[i] <- ifelse( EventTimes[i] <= 7, 1, 0 )
time[i] <- ifelse( EventTimes[i] < 7, Surv[i], 7 - Entry[i] )
}
modKM <- new("stmodelKM", coltrt=Txassign, survTime=time, censor=censor, trts=c(1,2), timePoint=4)
```

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