Description Usage Arguments Details Value

Compute Targeted Minimum Loss Estimate of Counterfactual Mean Under Stochastic Shift Intervention

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`data_internal` |
A |

`C` |
A |

`V` |
The covariates that are used in determining the sampling procedure
that gives rise to censoring. The default is |

`delta` |
A |

`ipcw_estim` |
An object providing the value of the censoring mechanism
evaluated across the full data. This object is passed in after being
constructed by a call to the internal function |

`Qn_estim` |
An object providing the value of the outcome evaluated after
imposing a shift in the treatment. This object is passed in after being
constructed by a call to the internal function |

`Hn_estim` |
An object providing values of the auxiliary ("clever")
covariate, constructed from the treatment mechanism and required for
targeted minimum loss-based estimation. This object object should be passed
in after being constructed by a call to |

`fluctuation` |
The method to be used in the submodel fluctuation step (targeting step) to compute the TML estimator. The choices are "standard" and "weighted" for where to place the auxiliary covariate in the logistic tilting regression. |

`max_iter` |
A |

`eif_reg_type` |
Whether a flexible nonparametric function ought to be
used in the dimension-reduced nuisance regression of the targeting step for
the censored data case. By default, the method used is a nonparametric
regression based on the Highly Adaptive Lasso (from hal9001).
Set this to |

`ipcw_fit_args` |
A |

`ipcw_efficiency` |
Whether to invoke an augmentation of the IPCW-TMLE
procedure that performs an iterative process to ensure efficiency of the
resulting estimate. The default is |

Invokes the procedure to construct a targeted minimum loss estimate (TMLE) of the counterfactual mean under a modified treatment policy.

S3 object of class `txshift`

containing the results of the
procedure to compute a TML estimate of the treatment shift parameter.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.