Description Usage Arguments Details Value Author(s) References Examples

Fits an Cox-Aalen survival model with missing data, with glm specification of probability of missingness.

1 2 3 4 5 6 7 8 | ```
cox.ipw(
survformula,
glmformula,
d = parent.frame(),
max.clust = NULL,
ipw.se = FALSE,
tie.seed = 100
)
``` |

`survformula` |
a formula object with the response on the left of a '~' operator, and the independent terms on the right as regressors. The response must be a survival object as returned by the ‘Surv’ function. Adds the prop() wrapper internally for using cox.aalen function for fitting Cox model. |

`glmformula` |
formula for "being" observed, that is not missing. |

`d` |
data frame. |

`max.clust` |
number of clusters in iid approximation. Default is all. |

`ipw.se` |
if TRUE computes standard errors based on iid decompositon of cox and glm model, thus should be asymptotically correct. |

`tie.seed` |
if there are ties these are broken, and to get same break the seed must be the same. Recommend to break them prior to entering the program. |

Taylor expansion of Cox's partial likelihood in direction of glm parameters using num-deriv and iid expansion of Cox and glm paramters (lava).

returns an object of type "cox.aalen". With the following arguments:

`iid` |
iid decomposition. |

`coef` |
missing data estiamtes for weighted cox. |

`var` |
robust pointwise variances estimates. |

`se` |
robust pointwise variances estimates. |

`se.naive` |
estimate of parametric components of model. |

`ties` |
list of ties and times with random noise to break ties. |

`cox` |
output from weighted cox model. |

Thomas Scheike

Paik et al.

1 2 | ```
### fit <- cox.ipw(Surv(time,status)~X+Z,obs~Z+X+time+status,data=d,ipw.se=TRUE)
### summary(fit)
``` |

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