Description Usage Arguments Details Value Author(s)

Compute the standard error associated to the predictions from Cox regression model using a first order von Mises expansion of the functional (cumulative hazard or survival).

1 2 3 |

`object` |
The fitted Cox regression model object either
obtained with |

`times` |
Vector of times at which to return the estimated hazard/survival. |

`nTimes` |
the length of the argument |

`type` |
One or several strings that match (either in lower or upper case or mixtures) one
or several of the strings |

`Lambda0` |
the baseline hazard estimate returned by |

`object.n` |
the number of observations in the dataset used to estimate the object. |

`object.time` |
the time to event of the observations used to estimate the object. |

`object.eXb` |
the exponential of the linear predictor relative to the observations used to estimate the object. |

`object.strata` |
the strata index of the observations used to estimate the object. |

`nStrata` |
the number of strata. |

`new.eXb` |
the linear predictor evaluated for the new observations |

`new.LPdata` |
the variables involved in the linear predictor for the new observations |

`new.strata` |
the strata indicator for the new observations |

`new.survival` |
the survival evaluated for the new observations |

`new.cumhazard` |
the cumulative hazard evaluated for the new observations |

`nVar` |
the number of variables that form the linear predictor |

`log.transform` |
Should the variance/influence function be computed on the log or log(-log) scale |

`export` |
can be "iid" to return the value of the influence function for each observation "se" to return the standard error for a given timepoint |

`store.iid` |
Implementation used to estimate the influence function and the standard error.
Can be |

Can also return the empirical influence function of the functionals cumulative hazard or survival or the sum over the observations of the empirical influence function.

`store.iid="full"`

compute the influence function for each observation at each time in the argument `times`

before computing the standard error / influence functions.
`store.iid="minimal"`

recompute for each subject specific prediction the influence function for the baseline hazard.
This avoid to store all the influence functions but may lead to repeated evaluation of the influence function.
This solution is therefore efficient more efficient in memory usage but may not be in term of computation time.

A list optionally containing the standard error for the survival, cumulative hazard and hazard.

Brice Ozenne [email protected], Thomas A. Gerds [email protected]

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