desirability {randomizeR}R Documentation

Desirability functions within the scope of clinical trials

Description

Illustrates the interplay between functions related to desirability indices.

Details

Currently, randomizeR encompasses the class of desirability functions introduced by Derringer and Suich (1980) and corresponding functions to evaluate and compare randomization sequences which have been assessed on the basis of desirability indices of specific issues:

Examples

# perform a comparison of randomization sequences from different randomization procedures 
# with the help of desirability functions

issue1 <- corGuess("CS")
issue2 <- chronBias(type = "linT", theta = 1/4, method = "exact")
RAR <- getAllSeq(rarPar(4))
BSD <- getAllSeq(bsdPar(4, mti = 2))
A1 <- assess(RAR, issue1, issue2, endp = normEndp(c(0,0), c(1,1)))
A2 <- assess(BSD, issue1, issue2, endp = normEndp(c(0,0), c(1,1)))

d1 <- derFunc(TV = 0.5, 0.75, 2)
d2 <- derFunc(0.05, c(0, 0.1), c(1, 1))

# apply the getDesScores function to the assessment output with the specified desirability
# functions to evaluate the behaviour of randomization sequences on a [0,1] scale

DesScore <- getDesScores(A1, d1, d2, weights = c(5/6, 1/6))
DesScore2 <- getDesScores(A2, d1, d2, weights = c(5/6, 1/6))

# plotting the desScores objects
plotDes(DesScore, quantiles = TRUE)
plotDes(DesScore2, quantiles = TRUE)

# summarize the results of getDesScore with respect to the statistic "mean"
evaluate(DesScore, DesScore2)

# plot the evaluation objects for a visualized comparison
plotEv(evaluate(DesScore, DesScore2))

# display which randomzation procedure produces more undesired randomization sequences 
# with respect to certain issues and desirability functions
probUnDes(DesScore)
probUnDes(DesScore2)


[Package randomizeR version 3.0.2 Index]