desirability function

Desirability functions within the scope of clinical trials

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:

• 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 behavior 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 probability of undesired randomization sequences with respect to certain issues and desirability functions.

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)