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

Assuming x is an output list object of class ICEuncrt, the default invocations of x or print(x) describe the bootstrap distribution of ICE uncertainty currently stored in x. An invocation of the form x10 <- print(x, lfact=10) increases the value of x item lambda by a factor of 10, describes that transformed bootstrap distribution, and stores it in object x10. When x item unit is cost, an invocation of the form xs <- print(x, swu=TRUE) describes the bootstrap distribution stored in x using effe units and stores the transformed distribution in object xs.

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`x` |
Required; Output list object of class ICEuncrt. |

`lfact` |
Optional; Positive factor multiplying the stored value of x item lambda. |

`swu` |
Optional; Logical value of TRUE or FALSE to control switching the stored value of x item unit between the 2 possibilities, cost and effe. |

`...` |
Optional; argument(s) passed on to plot(). |

After a single call to ICEuncrt() for an initial value of the Shadow Price of Health, lambda, and an initial choice of common display unit (cost or effe), multiple print() and/or plot() calls are usually made. Because the bootstrap distribution of ICE uncertainty is equivariant under changes in lambda, it is much faster to transform an existing Bootstrap ICE Uncertainty 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 units of measurement, respectively, without actually regenerating the bootstrap distribution via a new call to ICEuncrt().

Object of class ICEuncrt containing a possibly TRANSFORMED output list with items:

`df` |
Saved value of the name of the data.frame in the original call to ICEuncrt(). |

`lambda` |
Possibly changed, positive value of (lfact * x item lambda). |

`unit` |
Possibly switched value of x item unit, cost or effe. |

`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. |

`tb` |
R x 2 matrix of values of (DeltaEffe, DeltaCost) computed by transformation. |

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

Bob Obenchain <wizbob@att.net>

Obenchain RL. Issues and algorithms in cost-effectiveness inference. *Biopharmaceutical
Reports* 1997; **5(2)**: 1-7. Washington, DC: American Statistical Association.

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

Cook JR, Heyse JF. Use of an angular transformation for ratio estimation in cost-effectiveness
analysis. *Statistics in Medicine* 2000; **19**: 2989-3003.

`ICEuncrt`

, `ICEscale`

and `ICEwedge`

.

1 2 3 4 |

```
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 = 1.576
Observed Cost Difference = -2.899
Mean Bootstrap Cost Diff = -2.915
Incremental Cost-Effectiveness (ICE) Bivariate Bootstrap Uncertainty
Shadow Price = Lambda = 2.6
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 = 15.996
Mean Bootstrap Trtm Diff = 15.762
Observed Cost Difference = -2.899
Mean Bootstrap Cost Diff = -2.915
```

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