`iptw`

uses `gbm`

to estimate
propensity scores for sequential treatments.

1 2 3 4 5 6 7 8 9 10 |

`formula` |
Either a single formula (long format) or a list with formulas |

`data` |
The dataset, includes treatment assignment as well as covariates |

`timeInvariant` |
An optional formula (with no left-hand variable) specifying time-invariant chararacteristics. |

`n.trees` |
number of gbm iterations passed on to |

`stop.method` |
A method or methods of measuring and summarizing balance across
pretreatment variables. Current options are |

`cumulative` |
If |

`timeIndicators` |
For long format fits, a vector of times for each observation. |

`ID` |
For long format fits, a vector of numeric identifiers for unique analytic units. |

`priorTreatment` |
For long format fits, includes treatment levels from previous times if |

`...` |
Additional arguments that are passed to |

This function uses generalized boosted models to estimate inverse probability of treatment weights for sequential treatments.

Returns an object of class `iptw`

, a list containing

`psList` |
A list of ps objects with length equal to the number of time periods. |

`estimand` |
The specified estimand. |

`stop.methods` |
The stopping rules used to optimize iptw balance. |

`nFits` |
The number of ps objects (i.e., the number of distinct time points.) |

`uniqueTimes` |
The unique times in the specified model. |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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