R: Compute Bootstrap Distribution of ICE Uncertainty for given...

ICEuncrt

R Documentation

Compute Bootstrap Distribution of ICE Uncertainty for given Shadow Price of Health, lambda

Description

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.

Usage

ICEuncrt(df, trtm, xeffe, ycost, lambda = 1, unit = cost, R = 25000, seed = 0)

Arguments

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.

unit

Optional; unit character string containing either cost (default) or effe.

R

Optional; R positive integer value for the number of Bootstrap Replications
desired. Minimum allowed value is 50; default value is 25000.

seed

Optional; seed is an integer between 0 and 25000. A seed value of 0 causes
a random integer seed between 1 and 25000 to be generated. To reproduce results from a
previous invocation of ICEuncrt(), use the seed value saved in its output list object.

Details

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 units of measurement, respectively, without
actually generating a new bootstrap distribution via a call to ICEuncrt().

Value

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.

unit

Saved value of unit, 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.

Author(s)

Bob Obenchain <wizbob@att.net>

References

Obenchain RL. ICEplane: a windows application for incremental cost-effectiveness (ICE)
statistical inference. Copyright (c) Pharmaceutical Research and Manufacturers of America
(PhRMA.) http://members.iquest.net/~softrx/ 1997-2007.

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.

Obenchain RL. ICEinR.pdf Vignette-like documentation for ICEinfer
stored in the R library/ICEinfer/doc folder. 2009; 30 pages.

See Also

ICEscale, plot.ICEuncrt and print.ICEuncrt

Examples

data(dulxparx)
# Generating a bootstrap ICE uncertainty distribution is time consuming.
dpunc <- ICEuncrt(dulxparx, dulx, idb, ru, lambda=0.26)
plot(dpunc)
# Transforming an existing bootstrap ICE uncertainty distribution is fast.
dpuncX <- plot(dpunc, lfact=10)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(ICEinfer)
Loading required package: lattice
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ICEinfer/ICEuncrt.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ICEuncrt
> ### Title: Compute Bootstrap Distribution of ICE Uncertainty for given
> ### Shadow Price of Health, lambda
> ### Aliases: ICEuncrt
> ### Keywords: methods nonparametric robust
>
> ### ** Examples
>
> data(dulxparx)
> # Generating a bootstrap ICE uncertainty distribution is time consuming.
> dpunc <- ICEuncrt(dulxparx, dulx, idb, ru, lambda=0.26)
> plot(dpunc)
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 = 1.578
Observed Cost Difference = -2.899
Mean Bootstrap Cost Diff = -2.904
> # Transforming an existing bootstrap ICE uncertainty distribution is fast.
> dpuncX <- plot(dpunc, lfact=10)
>
>
>
>
>
> dev.off()
null device
1
>