| Title: | Two-Stage Adaptive Dose-Finding Clinical Trial Design |
|---|---|
| Description: | Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>. |
| Authors: | Alyssa Vanderbeek [aut, cre], Laura Cosgrove [ctb], Elizabeth Garrett-Mayer [ctb], Cody Chiuzan [ctb] |
| Maintainer: | Alyssa Vanderbeek <[email protected]> |
| License: | LGPL-3 |
| Version: | 2.0.1 |
| Built: | 2024-12-23 06:36:31 UTC |
| Source: | CRAN |
Calculate DLT probability corresponding to average nTTP for each dose
dlt.prob(dose, ntox, TOX, grade.thresh)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)# 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)
Function eff.stg1() uses a beta-binomial distribution to generate
outcomes (Ys) corresponding to acceptable dose assignments from stage 1.
eff.stg1(dose, dose.tox, p1, p2, K, coh.size, m, v, nbb = 100)eff.stg1(dose, dose.tox, p1, p2, K, coh.size, m, v, nbb = 100)
dose |
number of doses to be tested (scalar) |
dose.tox |
vector of true toxicities for each dose. Values range from 0 - 1. |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
m |
vector of mean efficacies per dose. Values range from 0 - 100. (e.g, T cell persistence - values b/w 5 and 80 per cent) |
v |
vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01) |
nbb |
binomial parameter (default = 100 cells per patient) |
List of efficacy outcomes for subjects enrolled during stage 1 (dose-escalation)
Y.safe - vector of efficacy outcomes for each subject assigned to an acceptable safe dose
d.safe - vector of dose allocation for each subject assigned to an acceptable safe dose
tox.safe - number of dose-limiting toxicities for each safe dose level
Y.alloc - vector of efficacy outcomes for all subjects from stage 1 (acceptable and unsafe doses)
d.alloc - vector of dose allocation for all subjects from stage 1 (acceptable and unsafe doses)
# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p2) and unacceptable (p1) DLT rates used for establishing safety p1 <- 0.40 p2 <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy (equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 eff.stg1(dose = dose, dose.tox = dose.tox, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m, v, nbb = 100)# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p2) and unacceptable (p1) DLT rates used for establishing safety p1 <- 0.40 p2 <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy (equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 eff.stg1(dose = dose, dose.tox = dose.tox, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m, v, nbb = 100)
Function eff.stg1.nTTP() uses a beta-binomial distribution to generate
outcomes (Ys) corresponding to acceptable dose assignments from stage 1.
eff.stg1.nTTP( dose, p1, p2, K, coh.size, m, v, nbb = 100, W, TOX, ntox, std.nTTP )eff.stg1.nTTP( dose, p1, p2, K, coh.size, m, v, nbb = 100, W, TOX, ntox, std.nTTP )
dose |
number of doses to be tested (scalar) |
p1 |
toxicity under null (unsafe nTTP). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe nTTP). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
m |
vector of mean efficacies per dose. Values range from 0 - 100. (e.g, T cell persistence - values b/w 5 and 80 per cent) |
v |
vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01) |
nbb |
binomial parameter (default = 100 cells per patient) |
W |
matrix defining burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 4 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
ntox |
number (integer) of different toxicity types |
std.nTTP |
the standard deviation of nTTP scores at each dose level (assumed constant across doses) |
List of efficacy outcomes for subjects enrolled during stage 1 (dose-escalation)
Y.safe - vector of efficacy outcomes for each subject assigned to an acceptable safe dose
d.safe - vector of dose allocation for each subject assigned to an acceptable safe dose
tox.safe - number of dose-limiting toxicities for each safe dose level
Y.alloc - vector of efficacy outcomes for all subjects from stage 1 (acceptable and unsafe doses)
d.alloc - vector of dose allocation for all subjects from stage 1 (acceptable and unsafe doses)
all_nttp - all observed nTTP values
# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Efficacy (equal) variance per dose v <- rep(0.01, 6) # Dose-efficacy curve m = c(10, 20, 30, 40, 70, 90) # Number of toxicity types ntox <- 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # standard deviation of nTTP values std.nTTP = 0.15 # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) eff.stg1.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m = m, v = v, nbb = 100, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Efficacy (equal) variance per dose v <- rep(0.01, 6) # Dose-efficacy curve m = c(10, 20, 30, 40, 70, 90) # Number of toxicity types ntox <- 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # standard deviation of nTTP values std.nTTP = 0.15 # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) eff.stg1.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m = m, v = v, nbb = 100, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)
Obtain average nTTP at each dose level
get.thresh(dose, ntox, W, TOX)get.thresh(dose, ntox, W, TOX)
dose |
number of doses to be tested (scalar) |
ntox |
number (integer) of different toxicity types (e.g, hematological, neurological, GI) |
W |
matrix defines burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 4 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
Vector of average nTTP for each dose level.
# Number of test doses dose = 6 # Number of toxicity types ntox <- 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # 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) get.thresh(dose = dose, ntox = ntox, W = W, TOX = TOX)# Number of test doses dose = 6 # Number of toxicity types ntox <- 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # 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) get.thresh(dose = dose, ntox = ntox, W = W, TOX = TOX)
Function LRtox() calculates the likelihood of safety for a single dose
and designates whether to escalate to the next dose (safe) or stop dose escalation and move onto stage 2 (unsafe).
LRtox(coh.size, ndlt, p1, p2, K = 2)LRtox(coh.size, ndlt, p1, p2, K = 2)
coh.size |
cohort size (number of patients) per dose (Stage 1) |
ndlt |
number of observed DLTs |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
List object that gives the likelihood ratio of safety and indicates whether to escalate to the next highest dose level, or stop dose escalation and move onto stage 2.
LRtox(coh.size = 3, ndlt = 2, p1 = 0.40, p2 = 0.15, K = 2) LRtox(coh.size = 3, ndlt = 1, p1 = 0.40, p2 = 0.15, K = 2)LRtox(coh.size = 3, ndlt = 2, p1 = 0.40, p2 = 0.15, K = 2) LRtox(coh.size = 3, ndlt = 1, p1 = 0.40, p2 = 0.15, K = 2)
(nTTP) Function LRtox.nTTP() calculates the likelihood of safety for a single dose
and designates whether to escalate to the next dose (safe) or stop dose escalation and move onto stage 2 (unsafe).
LRtox.nTTP(tox_grades, ntox, coh.size, W, p1, p2, K = 2, std.nTTP = 0.15)LRtox.nTTP(tox_grades, ntox, coh.size, W, p1, p2, K = 2, std.nTTP = 0.15)
tox_grades |
data frame of observed AE grades for each patient (rows) across all toxicity types (columns). e.g. for one patient, grades for 3 toxicity types might be c(3, 2, 4), where they experienced a grade 3 AE for tox type 1, grade 2 AE for tox type 2, etc. |
ntox |
number (integer) of different toxicity types |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
W |
matrix defining burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 5 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
std.nTTP |
the standard deviation of nTTP scores at each dose level (constant across doses) |
List object that gives the likelihood ratio of safety and indicates whether to escalate to the next highest dose level, or stop dose escalation and move onto stage 2.
ntox = 3 # three different types of toxicity coh.size = 3 # number of patients enrolled per dose # Observed AE grades for each patient on tested dose obs = data.frame(tox1 = c(3, 2, 4), tox2 = c(1, 1, 2), tox3 = c(2, 3, 3)) # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 LRtox.nTTP(obs, ntox, coh.size, W, p1, p2, K = 2, std.nTTP = 0.15)ntox = 3 # three different types of toxicity coh.size = 3 # number of patients enrolled per dose # Observed AE grades for each patient on tested dose obs = data.frame(tox1 = c(3, 2, 4), tox2 = c(1, 1, 2), tox3 = c(2, 3, 3)) # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 LRtox.nTTP(obs, ntox, coh.size, W, p1, p2, K = 2, std.nTTP = 0.15)
Results are displayed in a matrix format, where each row represents one trial simulation
nTTP.indiv.sim(W, TOX, ntox, dose)nTTP.indiv.sim(W, TOX, ntox, dose)
W |
matrix defining burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 5 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
ntox |
number (integer) of different toxicity types |
dose |
number of doses to be tested (scalar) |
List of the following objects:
sim.Y - estimated efficacy per each dose assignment
sim.d - dose assignment for each patient in the trial
Function rand.prob() calculates the updated randomization probabilities based on observed efficacies up to that point.
It also gives the dose allocation for the next enrolled patient based on these probabilities.
rand.prob(y.eff, d.safe)rand.prob(y.eff, d.safe)
y.eff |
vector of all efficacy outcomes for each dose allocation |
d.safe |
vector of dose assignment |
List object giving
Rand.Prob - randomization probability for each safe dose (from stage 1)
Next.Dose - the dose to enroll the next patient on
y.eff <- c(9, 1, 0, 34, 10, 27, 38, 42, 60, 75, 48, 62) d.safe <- c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4) rand.prob(y.eff, d.safe)y.eff <- c(9, 1, 0, 34, 10, 27, 38, 42, 60, 75, 48, 62) d.safe <- c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4) rand.prob(y.eff, d.safe)
Function rand.stg2() fits a linear regression for the continuous
efficacy outcomes,
computes the randomization probabilities/dose and allocates the next patient to a dose that
is considered acceptably safe and has the most promising efficacy. Dose safety is still
monitored using LR and doses
that become unacceptable are discarded (never re-visited).
rand.stg2( dose, dose.tox, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100 )rand.stg2( dose, dose.tox, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100 )
dose |
number of doses to be tested (scalar) |
dose.tox |
vector of true toxicities for each dose. Values range from 0 - 1. |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
m |
vector of mean efficacies per dose. Values range from 0 - 100. (e.g, T cell persistence - values b/w 5 and 80 per cent) |
v |
vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01) |
N |
total sample size for stages 1&2 |
stop.rule |
if only dose 1 safe, allocate up to 9 (default) patients at dose 1 to collect more info |
cohort |
cohort size (number of patients) per dose (Stage 2). Default is 1. |
samedose |
designates whether the next patient is allocated to the same dose as the previous patient. Default is TRUE. Function adjusts accordingly. |
nbb |
binomial parameter (default = 100 cells per patient) |
List of the following objects:
Y.final - vector of all efficacy outcomes (Ys) corresponding to dose assignments (Stages 1&2)
d.final - vector of all dose assignments(Stages 1&2)
If dose allocation stops early, put NAs in d.final and y.final until it reaches the total sample size.
# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy(equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 rand.stg2(dose, dose.tox, p_no, p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100)# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy(equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 rand.stg2(dose, dose.tox, p_no, p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100)
Function rand.stg2.nTTP() fits a linear regression for the continuous
efficacy outcomes, computes the randomization probabilities/dose and allocates the next patient
to a dose that is considered acceptably safe and has the highest efficacy. Dose safety
(with nTTP) is still monitored using LR and doses that become unacceptable are discarded
(never re-visited).
rand.stg2.nTTP( dose, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W, TOX, ntox, std.nTTP = 0.15 )rand.stg2.nTTP( dose, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W, TOX, ntox, std.nTTP = 0.15 )
dose |
number of doses to be tested (scalar) |
p1 |
toxicity under null (unsafe nTTP). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe nTTP). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
m |
vector of mean efficacies per dose. Values range from 0 - 100. (e.g, T cell persistence - values b/w 5 and 80 per cent) |
v |
vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01) |
N |
total sample size for stages 1&2 |
stop.rule |
if only dose 1 safe, allocate up to 9 (default) patients at dose 1 to collect more info |
cohort |
cohort size (number of patients) per dose (Stage 2). Default is 1. |
samedose |
designates whether the next patient is allocated to the same dose as the previous patient. Default is TRUE. Function adjusts accordingly. |
nbb |
binomial parameter (default = 100 cells per patient) |
W |
matrix defining burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 4 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
ntox |
number (integer) of different toxicity types |
std.nTTP |
the standard deviation of nTTP scores at each dose level (constant across doses) |
List of the following objects:
Y.final - vector of all efficacy outcomes (Ys) corresponding to dose assignments (Stages 1&2)
d.final - vector of all dose assignments(Stage 1&2)
n1 - Stage 1 sample size
If dose allocation stops early, put NAs in d.final and y.final until it reaches the total sample size.
# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Total sample size (stages 1&2) N <- 25 # Efficacy (equal) variance per dose v <- rep(0.01, 6) # Dose-efficacy curve m = c(10, 20, 30, 40, 70, 90) # Number of toxicity types ntox <- 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # Standard deviation of nTTP value std.nTTP = 0.15 # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) rand.stg2.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m = m, v = v, N = N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Total sample size (stages 1&2) N <- 25 # Efficacy (equal) variance per dose v <- rep(0.01, 6) # Dose-efficacy curve m = c(10, 20, 30, 40, 70, 90) # Number of toxicity types ntox <- 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # Standard deviation of nTTP value std.nTTP = 0.15 # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) rand.stg2.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m = m, v = v, N = N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)
Function safe.dose() distinguishes acceptable from unacceptable doses
safe.dose(dose, dose.tox, p1, p2, K, coh.size)safe.dose(dose, dose.tox, p1, p2, K, coh.size)
dose |
number of doses to be tested (scalar) |
dose.tox |
vector of true toxicities for each dose. Values range from 0 - 1. |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
List of the following objects:
alloc.safe - matrix of assignments only for acceptable doses (to be used in stage 2) and their corresponding toxicities
alloc.total - vector of all dose assignments from stage 1
n1 - total number of subjects allocated in stage 1
dose = 5 # Dose levels dose.tox <- c(0.05, 0.10, 0.15, 0.20, 0.30) # True toxicity per dose p1 = 0.40 # Unacceptable DLT rate p2 = 0.15 # Acceptable DLT rate K = 2 # Likelihood-ratio (LR) threshold coh.size = 3 # Assign 3 pts per dose in stage 1 safe.dose(dose = dose, dose.tox = dose.tox, p1 = p1, p2 = p2, K = K, coh.size = coh.size)dose = 5 # Dose levels dose.tox <- c(0.05, 0.10, 0.15, 0.20, 0.30) # True toxicity per dose p1 = 0.40 # Unacceptable DLT rate p2 = 0.15 # Acceptable DLT rate K = 2 # Likelihood-ratio (LR) threshold coh.size = 3 # Assign 3 pts per dose in stage 1 safe.dose(dose = dose, dose.tox = dose.tox, p1 = p1, p2 = p2, K = K, coh.size = coh.size)
Function safe.dose.nTTP() distinguishes acceptable from unacceptable doses
safe.dose.nTTP(dose, p1, p2, K, coh.size, W, TOX, ntox, std.nTTP = 0.15)safe.dose.nTTP(dose, p1, p2, K, coh.size, W, TOX, ntox, std.nTTP = 0.15)
dose |
number of doses to be tested (scalar) |
p1 |
toxicity under null (unsafe nTTP). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe nTTP). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
W |
matrix defining burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 4 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
ntox |
number (integer) of different toxicity types |
std.nTTP |
the standard deviation of nTTP scores at each dose level (constant across doses) |
List of the following objects:
alloc.safe - matrix of assignments only for acceptable doses (to be used in stage 2) and their corresponding toxicities
alloc.total - vector of all dose assignments from stage 1
n1 - total number of subjects allocated in stage 1
all_nttp - all observed nTTP values
# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Number of toxicity types ntox <- 3 # Standard deviation of nTTP values std.nTTP = 0.15 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) safe.dose.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Number of toxicity types ntox <- 3 # Standard deviation of nTTP values std.nTTP = 0.15 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) safe.dose.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)
Generate plots for estimated percent allocation and response per dose.
sim.plot(sims)sim.plot(sims)
sims |
output from sim.trials |
Error plots of estimated (1) percent allocation per dose, and (2) estimated response per dose.
# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean T-cell persistence per dose (%)) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy (equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule stop.rule <- 9 numsims = 100 set.seed(1) simulations = sim.trials(numsims = numsims, dose, dose.tox, p1 = p_no, p2 = p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100) # sim.plot(simulations)# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean T-cell persistence per dose (%)) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy (equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule stop.rule <- 9 numsims = 100 set.seed(1) simulations = sim.trials(numsims = numsims, dose, dose.tox, p1 = p_no, p2 = p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100) # sim.plot(simulations)
Results from simulated trials (using sim.trials() function)
displayed in tabular and/or graphical format
sim.summary(sims, print = TRUE)sim.summary(sims, print = TRUE)
sims |
output from sim.trials |
print |
logical specifying whether to print tables in console |
Printed tables and a list of the following objects:
pct.treated - IQR (25th percentile, median, 75th percentile) of percent of subjects treated at each dose level
efficacy - IQR of efficacy observed at each dose level
# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy(equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 simulations = sim.trials(numsims = 100, dose, dose.tox, p1 = p_no, p2 = p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100) summary = sim.summary(simulations)# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy(equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 simulations = sim.trials(numsims = 100, dose, dose.tox, p1 = p_no, p2 = p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100) summary = sim.summary(simulations)
Results are displayed in a matrix format, where each row represents one trial simulation.
sim.trials( numsims, dose, dose.tox, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100 )sim.trials( numsims, dose, dose.tox, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100 )
numsims |
number of simulated trials |
dose |
number of doses to be tested (scalar) |
dose.tox |
vector of true toxicities for each dose. Values range from 0 - 1. |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
m |
vector of mean efficacies per dose. Values range from 0 - 100. (e.g, T cell persistence - values b/w 5 and 80 per cent) |
v |
vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01) |
N |
total sample size for stages 1&2 |
stop.rule |
if only dose 1 safe, allocate up to 9 (default) patients at dose 1 to collect more info. |
cohort |
cohort size (number of patients) per dose (Stage 2). Default is 1. |
samedose |
designates whether the next patient is allocated to the same dose as the previous patient. Default is TRUE. Function adjusts accordingly. |
nbb |
binomial parameter (default = 100 cells per patient) |
List of the following objects:
sim.Y - estimated efficacy per each dose assignment
sim.d - dose assignment for each patient in the trial
safe.d - indicator of whether dose was declared safe
# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy(equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 sim.trials(numsims = 10, dose, dose.tox, p1 = p_no, p2 = p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100)# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety p_no <- 0.40 p_yes <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Vector of true mean efficacies per dose (here mean percent persistence per dose) m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox # Efficacy(equal) variance per dose v <- rep(0.01, 5) # Total sample size (stages 1&2) N <- 25 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 sim.trials(numsims = 10, dose, dose.tox, p1 = p_no, p2 = p_yes, K, coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100)
Results are displayed in a matrix format, where each row represents one trial simulation
sim.trials.nTTP( numsims, dose, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W, TOX, ntox, std.nTTP = 0.15 )sim.trials.nTTP( numsims, dose, p1, p2, K, coh.size, m, v, N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W, TOX, ntox, std.nTTP = 0.15 )
numsims |
number of simulated trials |
dose |
number of doses to be tested (scalar) |
p1 |
toxicity under null (unsafe nTTP). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe nTTP). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
m |
vector of mean efficacies per dose. Values range from 0 - 100. (e.g, T cell persistence - values b/w 5 and 80 per cent) |
v |
vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01) |
N |
total sample size for stages 1&2 |
stop.rule |
if only dose 1 safe, allocate up to 9 (default) patients at dose 1 to collect more info |
cohort |
cohort size (number of patients) per dose (Stage 2). Default is 1. |
samedose |
designates whether the next patient is allocated to the same dose as the previous patient. Default is TRUE. Function adjusts accordingly. |
nbb |
binomial parameter (default = 100 cells per patient) |
W |
matrix defining burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 4 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
ntox |
number (integer) of different toxicity types |
std.nTTP |
the standard deviation of nTTP scores at each dose level (constant across doses) |
List of the following objects:
sim.Y - estimated efficacy per each dose assignment
sim.d - dose assignment for each patient in the trial
safe.d - indicator of whether dose was declared safe
# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Total sample size (stages 1&2) N <- 25 # Efficacy (equal) variance per dose v <- rep(0.01, 6) # Dose-efficacy curve m = c(10, 20, 30, 40, 70, 90) # Number of toxicity types ntox = 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # Standard deviation of nTTP values std.nTTP = 0.15 # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) sim.trials.nTTP(numsims = 10, dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m = m, v = v, N = N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Total sample size (stages 1&2) N <- 25 # Efficacy (equal) variance per dose v <- rep(0.01, 6) # Dose-efficacy curve m = c(10, 20, 30, 40, 70, 90) # Number of toxicity types ntox = 3 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # Standard deviation of nTTP values std.nTTP = 0.15 # 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, 0.791, 0.172, 0.032, 0.004, 0.001, 0.758, 0.180, 0.043, 0.010, 0.009, 0.685, 0.190, 0.068, 0.044, 0.013, 0.662, 0.200, 0.078, 0.046, 0.014, 0.605, 0.223, 0.082, 0.070, 0.020), nrow = 6, byrow = TRUE) TOX[, , 2] = matrix(c(0.970, 0.027, 0.002, 0.001, 0.000, 0.968, 0.029, 0.002, 0.001, 0.000, 0.813, 0.172, 0.006, 0.009, 0.000, 0.762, 0.183, 0.041, 0.010, 0.004, 0.671, 0.205, 0.108, 0.011, 0.005, 0.397, 0.258, 0.277, 0.060, 0.008), nrow = 6, byrow = TRUE) TOX[, , 3] = matrix(c(0.930, 0.060, 0.005, 0.001, 0.004, 0.917, 0.070, 0.007, 0.001, 0.005, 0.652, 0.280, 0.010, 0.021, 0.037, 0.536, 0.209, 0.031, 0.090, 0.134, 0.015, 0.134, 0.240, 0.335, 0.276, 0.005, 0.052, 0.224, 0.372, 0.347), nrow = 6, byrow = TRUE) sim.trials.nTTP(numsims = 10, dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, m = m, v = v, N = N, stop.rule = 9, cohort = 1, samedose = TRUE, nbb = 100, W = W, TOX = TOX, ntox = ntox, std.nTTP = std.nTTP)
This is a sample array of toxicity probabilities to be used for trial simulations with nTTP as the toxicity endpoint. In this example, we have 3 toxicity types, 6 test doses, and 5 AE grades (0-4). See the nTTP_simulation_example vignette for more details.
data("TOX")data("TOX")
The format is: num [1:6, 1:5, 1:3] 0.791 0.738 0.685 0.662 0.605 0.39 0.172 0.195 0.19 0.2 ...
https://pubmed.ncbi.nlm.nih.gov/30403559/
data(TOX) TOXdata(TOX) TOX
Gives toxicity profile (number of dose-limiting toxicities) and likelihood ratio per dose based on binary toxicity.
tox.profile(dose, dose.tox, p1, p2, K, coh.size)tox.profile(dose, dose.tox, p1, p2, K, coh.size)
dose |
number of doses to be tested (scalar) |
dose.tox |
vector of true toxicities for each dose. Values range from 0 - 1. |
p1 |
toxicity under null (unsafe DLT rate). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
4-column matrix containing dose assignment, dose-limiting toxicities at each dose, cohort number, and likelihood ratio.
# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p2) and unacceptable (p1) DLT rates used for establishing safety p1 <- 0.40 p2 <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 tox.profile(dose = dose, dose.tox = dose.tox, p1 = p1, p2 = p2, K = K, coh.size = coh.size)# Number of pre-specified dose levels dose <- 5 # Vector of true toxicities associated with each dose dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45) # Acceptable (p2) and unacceptable (p1) DLT rates used for establishing safety p1 <- 0.40 p2 <- 0.15 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts. stop.rule <- 9 tox.profile(dose = dose, dose.tox = dose.tox, p1 = p1, p2 = p2, K = K, coh.size = coh.size)
The normalized total toxicity profiles (nTTP) are calculated by combining multiple toxicity grades and their weights. The nTTPs are considered a quasi-continuous toxicity measure that follows a normal distribution truncated to [0, 1]. The likelihood ratio per dose are based on nTTP toxicity.
tox.profile.nTTP(dose, p1, p2, K, coh.size, ntox, W, TOX, std.nTTP = 0.15)tox.profile.nTTP(dose, p1, p2, K, coh.size, ntox, W, TOX, std.nTTP = 0.15)
dose |
number of doses to be tested (scalar) |
p1 |
toxicity under null (unsafe nTTP). Values range from 0 - 1. |
p2 |
toxicity under alternative (safe nTTP). Values range from 0 - 1; p1 > p2 |
K |
threshold for LR. Takes integer values: 1,2,...(recommended K=2) |
coh.size |
cohort size (number of patients) per dose (Stage 1) |
ntox |
number (integer) of different toxicity types (e.g, hematological, neurological, GI) |
W |
matrix defines burden weight of each grade level for all toxicity types. The dimensions are ntox rows by 5 columns (for grades 0-4). See Ezzalfani et al. (2013) for details. |
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). Probabilities across each dose (rows) must sum to 1. See Ezzalfani et al. (2013) for details. |
std.nTTP |
the standard deviation of nTTP scores at each dose level (constant across doses) |
mnTTP - 4-column matrix containing dose assignment, mean nTTP at each dose, cohort number, and likelihood ratio.
all_nttp - all observed nTTP values
# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Number of toxicity types ntox <- 3 # Standard deviation of nTTP values std.nTTP = 0.15 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # 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) tox.profile.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, ntox = ntox, W = W, TOX = TOX, std.nTTP = std.nTTP)# Number of pre-specified dose levels dose <- 6 # Acceptable (p2) and unacceptable nTTP values p1 <- 0.35 p2 <- 0.10 # Likelihood-ratio (LR) threshold K <- 2 # Cohort size used in stage 1 coh.size <- 3 # Number of toxicity types ntox <- 3 # Standard deviation of nTTP values std.nTTP = 0.15 # Toxicity burden weight matrix W = matrix(c(0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 1 0, 0.5, 0.75, 1.0, 1.5, # Burden weight for grades 0-4 for toxicity 2 0, 0.00, 0.00, 0.5, 1), # Burden weight for grades 0-4 for toxicity 3 nrow = ntox, byrow = TRUE) # 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) tox.profile.nTTP(dose = dose, p1 = p1, p2 = p2, K = K, coh.size = coh.size, ntox = ntox, W = W, TOX = TOX, std.nTTP = std.nTTP)