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:
-
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)