dlt.prob: Calculate DLT probability corresponding to average nTTP for...
In iAdapt: Two-Stage Adaptive Dose-Finding Clinical Trial Design

Description Usage Arguments Value Examples

Calculate DLT probability corresponding to average nTTP for each dose

1	dlt.prob(dose, ntox, TOX, grade.thresh)

`dose`	number of doses to be tested (scalar)
`ntox`	number (integer) of different toxicity types (e.g, hematological, neurological, GI)
`TOX`	matrix array of toxicity probabilities. There should be ntox matrices. Each matrix represents one toxicity type, where probabilities of each toxicity grade are specified across each dose. Each matrix has the same dimensions: n rows, representing number of doses, and 5 columns (for grades 0-4, since the probability of a grade 0 event may not be 0). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details.
`grade.thresh`	grade (0-4) at which each toxicity type qualifies as a DLT

ptox - Vector of DLT probabilities per dose.

# Number of test doses
dose = 6

# Number of toxicity types
ntox <- 3
           
# Array of toxicity event probabilities
TOX = array(NA, c(dose, 5, ntox)) 

TOX[, , 1] = matrix(c(0.823, 0.152, 0.022, 0.002, 0.001,  #prob of tox for dose 1 and tox type 1
                      0.791, 0.172, 0.032, 0.004, 0.001,  #prob of tox for dose 2 and tox type 1
                      0.758, 0.180, 0.043, 0.010, 0.009,  #prob of tox for dose 3 and tox type 1
                      0.685, 0.190, 0.068, 0.044, 0.013,  #prob of tox for dose 4 and tox type 1
                      0.662, 0.200, 0.078, 0.046, 0.014,  #prob of tox for dose 5 and tox type 1
                      0.605, 0.223, 0.082, 0.070, 0.020), #prob of tox for dose 6 and tox type 1
                      nrow = 6, byrow = TRUE)
TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000,  #prob of tox for dose 1 and tox type 2
                      0.968, 0.029, 0.002, 0.001, 0.000,  #prob of tox for dose 2 and tox type 2
                      0.813, 0.172, 0.006, 0.009, 0.000,  #prob of tox for dose 3 and tox type 2
                      0.762, 0.183, 0.041, 0.010, 0.004,  #prob of tox for dose 4 and tox type 2
                      0.671, 0.205, 0.108, 0.011, 0.005,  #prob of tox for dose 5 and tox type 2
                      0.397, 0.258, 0.277, 0.060, 0.008), #prob of tox for dose 6 and tox type 2
                      nrow = 6, byrow = TRUE)
TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004,  #prob of tox for dose 1 and tox type 3
                      0.917, 0.070, 0.007, 0.001, 0.005,  #prob of tox for dose 2 and tox type 3
                      0.652, 0.280, 0.010, 0.021, 0.037,  #prob of tox for dose 3 and tox type 3
                      0.536, 0.209, 0.031, 0.090, 0.134,  #prob of tox for dose 4 and tox type 3
                      0.015, 0.134, 0.240, 0.335, 0.276,  #prob of tox for dose 5 and tox type 3
                      0.005, 0.052, 0.224, 0.372, 0.347), #prob of tox for dose 6 and tox type 3
                      nrow = 6, byrow = TRUE)
                      
# Grades at which each tox type qualifies as DLT
grade.thresh = c(3, 3, 4)

dlt.prob(dose = dose, ntox = ntox, TOX = TOX, grade.thresh = grade.thresh)