ICEscale: ICEscale() functions compute or print ICE Statistical...
In ICEinfer: Incremental Cost-Effectiveness Inference using Two Unbiased Samples

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

ICEscale() computes Summary Statistics for 2-sample, 2-variable inference where one variable is a measure of effectiveness (higher values are better) and the other variable is a measure of 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.

1	ICEscale(df, trtm, xeffe, ycost, lambda = 1, ceunit = "cost")

`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; `lambda` strictly positive value for the Shadow Price of Health.
`ceunit`	Optional; `ceunit` character string containing either cost (default) or effe.

After an initial call with the default value of lambda = 1, multiple additional calls to ICEscale() with different numerical values for lambda are usually made at the very beginning of analyses using other functions from the ICEinfer package. For example, the statistical choice for lambda assures that the DeltaEffe and DeltaCost mean treatment differences (new minus std) will have approximately equal variability when expressed in either cost or effe ceunits. The power of ten value of lambda that is closest to the statistical value for lambda assures use of ceunits that, except for the position of the decimal point, are identical to the cost/effectiveness ratio implied by the scales in which data values are stored within the input data.frame.

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

`trtm`	Saved name of the treatment indicator within the input data.frame.
`xeffe`	Saved name of the treatment effectiveness variable within the input data.frame.
`ycost`	Saved name of the treatment cost variable within the input data.frame.
`effcst`	Saved value of the sorted 3-variable (trtm,effe,cost) data.frame.
`lambda`	Value for the Shadow Price of Health, lambda, input to ICEscals().
`t1`	Observed values of (DeltaEffe, DeltaCost) when each distinct patient is sampled exactly once.
`s1`	Observed values for the standard deviations of (DeltaEffe, DeltaCost) when each distinct patient is sampled exactly once.
`slam`	Statistical Shadow Price computed as s1[2]/s1[1] and rounded to digits = 3.
`potlam`	Power-of-Ten Shadow Price computed as 10\^(as.integer(log10(slam))).

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.

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

ICEscale, plot.ICEuncrt and print.ICEuncrt

1 2	data(dulxparx) ICEscale(dulxparx, dulx, idb, ru)

Loading required package: lattice

Incremental Cost-Effectiveness (ICE) Lambda Scaling Statistics

Specified Value of Lambda   = 1
Cost and Effe Differences are both expressed in cost units

Effectiveness variable Name = idb
     Cost     variable Name = ru
  Treatment   factor   Name = dulx
New treatment level is = 1 and Standard level is = 0 

Observed  Treatment Diff = 6.152
Std. Error of Trtm Diff  = 8.186 

Observed Cost Difference = -2.899
Std. Error of Cost Diff  = 3.096 

Observed  ICE  Ratio     = -0.471 

Statistical Shadow Price = 0.378
Power-of-Ten Shadow Price= 1