print.ICEuncrt: Summary Statistics for a possibly Transformed Bootstrap...

In ICEinfer: Incremental Cost-Effectiveness Inference using Two Unbiased Samples

Description

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.

Usage

  ## S3 method for class 'ICEuncrt'
print(x, lfact = 1, swu = FALSE, ...)

Arguments

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().

Details

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().

Value

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.

Author(s)

Bob Obenchain <[e-mail]>

References

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.

See Also

ICEuncrt, ICEscale and ICEwedge.

Examples

  data(dpunc)
  dpunc
  # Transformation of bootstrap distributions is fast.
  dpuncX <- print(dpunc, lfact=10)

Example output

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 

ICEinfer documentation built on Oct. 23, 2020, 8:31 p.m.