Desirability functions within the scope of clinical trials
Illustrates the interplay between functions related to desirability indices.
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.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.