Description Usage Arguments Details Value Author(s) References See Also Examples

ICEuncrt() uses bootstrap resampling (with replacement) to compute the distribution of uncertainty for 2-sample, 2-variable statistical inference. The 2 variables must be measures of effectiveness (higher values are better) and cost (lower values are better). The 2 samples are of patients receiving only 1 of the 2 possible treatments. The treatment called new is the one with the higher numerical level for the specified treatment indicator variable, while the treatment called std corresponds to the lower numerical level. The pivotal statistic for inference is (DeltaEffe, DeltaCost), which are the head-to-head mean differences for new treatment minus std treatment. Each sample is assumed to provide unbiased estimates of the overall expected effectiveness and cost for that treatment.

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`df` |
Required; Existing data.frame object containing the trtm, xeffe and ycost variables. |

`trtm` |
Required; Name of the treatment indicator variable contained within the df data.frame that assumes one of only two different numerical values for each patient. |

`xeffe` |
Required; Name of the treatment effectiveness variable within the df data.frame. |

`ycost` |
Required; Name of the treatment cost variable within the df data.frame. |

`lambda` |
Optional; |

`ceunit` |
Optional; |

`R` |
Optional; |

`seed` |
Optional; |

A single call to ICEuncrt() is usually made for a particular value of the Shadow Price of Health, lambda. Alternative statistical choices for lambda can be suggested by making calls to ICEscale() with different values for lambda. Because the bootstrap distribution of ICE uncertainty is equivariant under changes in lambda, it is much faster to transform an existing bootstrap distribution than to generate a new one for a different value of lambda. The print.ICEuncrt() and plot.ICEuncrt() functions thus have 2 special parameters, lfact and swa, that can change lambda and switch the ceunit of measurement, respectively, without actually generating a new bootstrap distribution via a call to ICEuncrt().

Object of class ICEuncrt containing an output list with the following items:

`df` |
Saved value of the name of the data.frame input to ICEuncrt. |

`lambda` |
Saved positive value of lambda input to ICEuncrt. |

`ceunit` |
Saved value of ceunit (cost or effe) input to ICEuncrt. |

`R` |
Saved integer value for number of bootstrap replications input to ICEuncrt. |

`trtm` |
Saved name of the treatment indicator within the df data.frame. |

`xeffe` |
Saved name of the treatment effectiveness variable within the df data.frame. |

`ycost` |
Saved name of the treatment cost variable within the df data.frame. |

`effcst` |
Saved value of the sorted 3-variable (trtm,effe,cost) data.frame. |

`t1` |
Observed value of (DeltaEffe, DeltaCost) when each patient is included exactly once. |

`t` |
R x 2 matrix of values of (DeltaEffe, DeltaCost) computed from bootstrap resamples. |

`seed` |
Saved value of the seed used to start pseudo random number generation. |

Bob Obenchain <wizbob@att.net>

Obenchain RL, Melfi CA, Croghan TW, Buesching DP. Bootstrap analyses of cost-effectiveness
in antidepressant pharmacotherapy. *PharmacoEconomics* 1997; **17**: 1200-1206.

Obenchain RL. Resampling and multiplicity in cost-effectiveness inference. *Journal of
Biopharmaceutical Statistics* 1999; **9(4)**: 563-582.

`ICEscale`

, `plot.ICEuncrt`

and `print.ICEuncrt`

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```
Loading required package: lattice
Incremental Cost-Effectiveness (ICE) Bivariate Bootstrap Uncertainty
Shadow Price = Lambda = 0.26
Bootstrap Replications, R = 25000
Effectiveness variable Name = idb
Cost variable Name = ru
Treatment factor Name = dulx
New treatment level is = 1 and Standard level is = 0
Cost and Effe Differences are both expressed in cost units
Observed Treatment Diff = 1.6
Mean Bootstrap Trtm Diff = 1.607
Observed Cost Difference = -2.899
Mean Bootstrap Cost Diff = -2.925
```

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