Description Usage Arguments Details Author(s) Examples

View source: R/binomial.regression.R

Under the standard causal assumptions we can estimate the average treatment effect E(Y(1) - Y(0)). We need Consistency, ignorability ( Y(1), Y(0) indep A given X), and positivity.

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`formula` |
formula with outcome (see |

`data` |
data frame |

`cause` |
cause of interest |

`time` |
time of interest |

`beta` |
starting values |

`treat.model` |
logistic treatment model given covariates |

`cens.model` |
only stratified cox model without covariates |

`offset` |
offsets for partial likelihood |

`weights` |
for score equations |

`cens.weights` |
censoring weights |

`se` |
to compute se's with IPCW adjustment, otherwise assumes that IPCW weights are known |

`kaplan.meier` |
uses Kaplan-Meier for IPCW in contrast to exp(-Baseline) |

`cens.code` |
gives censoring code |

`no.opt` |
to not optimize |

`method` |
for optimization |

`augmentation` |
to augment binomial regression |

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

The first covariate in the specification of the competing risks regression model must be the treatment effect that is binary. This is then model using a logistic regresssion using the standard binary double robust estimating equations that are then IPCW censoring adjusted using binomial regression.

Also computes the ATT and ATC, average treatment effect on the treated (ATT), E(Y(1) - Y(0) | A=1), and non-treated, respectively.

Rather than binomial regression we also consider a IPCW weighted version of standard logistic regression logitIPCWATE.

Thomas Scheike

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