R: Data for the High Uncertainty numerical example of Obenchain...

dulxparx

R Documentation

Data for the High Uncertainty numerical example of Obenchain et al. (2005)

Description

The data are from two arms of a double-blind clinical trial in which 91 patients
were randomized to the SNRI duloxetine 80 mg/d (40 mg BID) and 87 patients were randomized to
the SSRI paroxetine 20 mg/d for treatment of major depressive disorder (MDD). Missing-data-
imputation and sensitivity-analyses were needed to make meaningful cost-effectiveness
comparisons in this study.

Usage

data(dulxparx)

Format

A data frame of 3 variables on 178 patients; no NAs.

idb

This measure of overall effectiveness is integrated decrease in HAMD-17
score from baseline to endpoint, Hamilton (1967). This is a (signed) area-under-the-curve
measure with larger values more favorable. Missing values were imputed via the MMRM models
reported in Goldstein et al. (2004).

ru

Patient self-reported health-care resource utilization above and beyond that
provided within study protocol was collected using the Resource Utilization Survey,
Copley-Merriman et al. (1992), with published 1998 dollars-per-unit costs, Schoenbaum et al. (2001),
rounded to the nearest 50 dollars. Dollars/week were then calculated by multiplying (total accumulated
cost) for a patient by 7 and dividing by the (total days of cost accumulation) for that
patient. For patients who discontinued early, this is Average-Value-Carried-Forward
imputation.

Copley-Merriman C, Egbuonu-Davis L, Kotsanos JG, Conforti P, Franson T, Gordon G. Clinical
economics: a method for prospective health resource data collection. Pharmacoeconomics
1992; 1(5): 370–376.

Goldstein DJ, Lu Y, Detke MJ, Wiltse C, Mallincrodt C, Demitrack MA. Duloxetine in the
treatment of depression - A double-blind, placebo-controlled comparison with paroxetine.
J Clin Psychopharmacol 2004; 24: 389–399.

Hamilton M. Development of a rating scale for primary depressive illness. British
Journal of Social and Clinical Psychology 1967; 6: 278–296.

Obenchain RL, Robinson RL, Swindle RW. Cost-effectiveness inferences from bootstrap quadrant
confidence levels: three degrees of dominance. J Biopharm Stat 2005; 15(3):
419–436.

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

Schoenbaum M, Unutzer J, Sherbourne C, Duan N, Rubenstein LV, Miranda J, Meredith LS, Carney
MF, Wells K. Cost-effectiveness of practice-initiated quality improvement for depression:
results of a randomized controlled trial. JAMA 2001; 286(11): 1325–1330.

Examples

# Demo of ICEinfer functionality on the dulxparx dataset...
demo(dulxparx)

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/dulxparx.Rd_%03d_medium.png", width=480, height=480)
> ### Name: dulxparx
> ### Title: Data for the High Uncertainty numerical example of Obenchain et
> ### al. (2005)
> ### Aliases: dulxparx
> ### Keywords: datasets
>
> ### ** Examples
>
> # Demo of ICEinfer functionality on the dulxparx dataset...
> demo(dulxparx)
demo(dulxparx)
---- ~~~~~~~~
> require(ICEinfer)
> # input the dulxparx data of Obenchain et al. (2000).
> data(dulxparx)
> # Effectiveness = idb, Cost = ru, trtm = dulx where
> # dulx = 1 ==> Duloxetine treatment and dulx = 0 ==> Paroxetine treatment
> #
> # Display of Lambda => Shadow Price Summary Statistics...
> ICEscale(dulxparx, dulx, idb, ru)
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
> ICEscale(dulxparx, dulx, idb, ru, lambda=0.26)
Incremental Cost-Effectiveness (ICE) Lambda Scaling Statistics
Specified Value of Lambda = 0.26
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 = 1.6
Std. Error of Trtm Diff = 2.128
Observed Cost Difference = -2.899
Std. Error of Cost Diff = 3.096
Observed ICE Ratio = -1.812
Statistical Shadow Price = 1.455
Power-of-Ten Shadow Price= 1
> # Bootstrap ICE Uncertainty calculations can be time consuming...
> dpunc <- ICEuncrt(dulxparx, dulx, idb, ru, R = 10000, lambda=0.26)
> dpunc
Incremental Cost-Effectiveness (ICE) Bivariate Bootstrap Uncertainty
Shadow Price = Lambda = 0.26
Bootstrap Replications, R = 10000
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 = 1.59
Observed Cost Difference = -2.899
Mean Bootstrap Cost Diff = -2.905
> # Display the Bootstrap ICE Uncertainty Distribution...
> plot(dpunc)
Incremental Cost-Effectiveness (ICE) Bivariate Bootstrap Uncertainty
Shadow Price = Lambda = 0.26
Bootstrap Replications, R = 10000
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 = 1.59
Observed Cost Difference = -2.899
Mean Bootstrap Cost Diff = -2.905
> dpwdg <- ICEwedge(dpunc)
> dpwdg
ICEwedge: Incremental Cost-Effectiveness Bootstrap Confidence Wedge...
Shadow Price of Health, lambda = 0.26
Shadow Price of Health Multiplier, lfact = 1
ICE Differences in both Cost and Effectiveness expressed in cost units.
ICE Angle of the Observed Outcome = -16.107
ICE Ratio of the Observed Outcome = -1.812
Count-Outwards Central ICE Angle Order Statistic = 4874 of 10000
Counter-Clockwise Upper ICE Angle Order Statistic = 9624
Counter-Clockwise Upper ICE Angle = 111.663
Counter-Clockwise Upper ICE Ratio = 2.31791
Clockwise Lower ICE Angle Order Statistic = 124
Clockwise Lower ICE Angle = -149.639
Clockwise Lower ICE Ratio = -0.26121
ICE Angle Computation Perspective = alibi
Confidence Wedge Subtended ICE Polar Angle = 261.303
> opar <- par(ask = dev.interactive(orNone = TRUE))
> # Click within graphics window to display the Bootstrap 95% Confidence Wedge...
> plot(dpwdg)
> # Computing VAGR Acceptability and ALICE Curves...
> dpacc <- ICEalice(dpwdg)
> plot(dpacc)
> # Color Interior of Confidence Wedge with LINEAR Economic Preferences...
> dpcol <- ICEcolor(dpwdg, gamma=1)
> plot(dpcol)
> # Increase Lambda and Recolor Confidence Wedge with NON-Linear Preferences...
> dpcol <- ICEcolor(dpwdg, lfact=10)
> plot(dpcol)
> # Decrease Lambda and Recolor Confidence Wedge with LINEAR Preferences...
> dpcol <- ICEcolor(dpwdg, lfact=10, gamma=1)
> plot(dpcol)
> par(opar)
>
>
>
>
>
> dev.off()
null device
1
>