# Conditional survival probabilities based on the Kaplan-Meier weights.

### Description

Provides estimates for the conditional survival probabilities based on Kaplan-Meier weighted estimators.

### Usage

1 2 |

### Arguments

`object` |
An object of class "survCS". |

`x` |
The first time for obtaining estimates for the conditional survival probabilities. |

`y` |
The total time for obtaining estimates for the conditional survival probabilities. |

`conf` |
Provides pointwise confidence bands. Defaults to FALSE. |

`n.boot` |
The number of bootstrap samples. Defaults to 1000 samples. |

`conf.level` |
Level of confidence. Defaults to 0.95 (corresponding to 95%). |

`lower.tail` |
logical; if FALSE (default), probabilities are P(T > y|T1 > x) otherwise, P(T > y|T1 <= x). |

`cluster` |
A logical value. If |

`ncores` |
An integer value specifying the number of cores to be used
in the parallelized procedure. If |

### Value

An object of class "KMW" and of the class "surv". "KMW" objects are implemented as a list with elements:

`est` |
data.frame with estimates of the conditional probabilities. |

`estimate` |
Estimates of the conditional survival probability. |

`LCI` |
The lower conditional survival probabilities of the interval. |

`UCI` |
The upper conditional survival probabilities of the interval. |

`conf.level` |
Level of confidence. |

`y` |
The total time for obtaining the estimates of the conditional survival probabilities. |

`x` |
The first time for obtaining the estimates of the conditional survival probabilities. |

`conf` |
logical; if FALSE (default) the pointwise confidence bands are not given. |

### Author(s)

Luis Meira-Machado and Marta Sestelo

### References

L. Meira-Machado, M. Sestelo, and A. Goncalves. Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study. Biometrical Journal, 2016.

### See Also

`survLDM`

, `survPLDM`

and `survIPCW`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
data(colonCS)
obj <- with(colonCS, survCS(time1, event1, Stime, event))
#P(T>y|T1>x)
survKMW(obj, x = 365, y = 730)
#P(T>y|T1<=x)
survKMW(obj, x = 365, y = 730, lower.tail = TRUE)
survKMW(obj, x = 365, y = c(730, 1095, 1460))
survKMW(obj, x = 365, y = c(730, 1095, 1460), lower.tail = TRUE)
survKMW(obj, x = 365, y = 730, conf = TRUE, n.boot = 100, conf.level = 0.95,
cluster = FALSE)
survKMW(obj, x = 365, y = c(730, 1095, 1460), conf = TRUE, n.boot = 100,
conf.level = 0.95, cluster = FALSE)
res <- survKMW(obj, x = 365, y = c(730, 1095, 1460), conf = TRUE,
n.boot = 100, conf.level = 0.95, cluster = FALSE)
res$est
``` |

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