Description Usage Arguments Author(s) Examples

Estimation of concordance in bivariate competing risks data

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`formula` |
Formula with left-hand-side being a |

`data` |
Data frame |

`cause` |
Causes (default (1,1)) for which to estimate the bivariate cumulative incidence |

`cens` |
The censoring code |

`causes` |
causes |

`indiv` |
indiv |

`strata` |
Strata |

`id` |
Clustering variable |

`num` |
num |

`max.clust` |
max number of clusters in comp.risk call for iid decompostion, max.clust=NULL uses all clusters otherwise rougher grouping. |

`marg` |
marginal cumulative incidence to make stanard errors for same clusters for subsequent use in casewise.test() |

`se.clusters` |
to specify clusters for standard errors. Either a vector of cluster indices or a column name in |

`prodlim` |
prodlim to use prodlim estimator (Aalen-Johansen) rather than IPCW weighted estimator based on comp.risk function.These are equivalent in the case of no covariates. |

`messages` |
Control amount of output |

`model` |
Type of competing risk model (default is Fine-Gray model "fg", see comp.risk). |

`return.data` |
Should data be returned (skipping modeling) |

`uniform` |
to compute uniform standard errors for concordance estimates based on resampling. |

`conservative` |
for conservative standard errors, recommended for larger data-sets. |

`resample.iid` |
to return iid residual processes for further computations such as tests. |

`...` |
Additional arguments to lower level functions |

Thomas Scheike, Klaus K. Holst

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mets documentation built on May 31, 2017, 1:52 a.m.

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