desirability: Desirability functions within the scope of clinical trials

Description

Illustrates the interplay between functions related to desirability indices.

Arguments

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:

derFunc represents the class of desirability functions according to Derringer-Suich (1980).
getDesScores can be applied to an object of class assessment together with prespecified desirability functions to compare the behaviour of randomization sequences (on a common scale [0,1]).
plotDes plots a desScores object on a radar chart.
evaluate performs a comparison of sequences from different randomization sequences on the basis of object of the class desScores.
plotEv plots an evaluation object on a radar chart.
probUnDes computes the proability of undesired randomization sequences with respect to certain issues and desirability functions.

Examples

Run this code

# NOT RUN {
# Suppose we would like to perform a comparison of 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))

# By applying the \code{getDesScores} function to the assessment output together 
# with the specified desirability functions the behaviour of randomization sequences 
# is evaluated and scaled to [0,1]:
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)

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

# Plotting the evaluation objects allows a visualized comparison:
plotEv(evaluate(DesScore, DesScore2))

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

# }

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