sim.trials.nTTP: Simulate full trial (both stages) x times when using nTTP to...

In iAdapt: Two-Stage Adaptive Dose-Finding Clinical Trial Design

Description

Results are displayed in a matrix format, where each row represents one trial simulation

Usage

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
)

Arguments

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)

Value

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

Examples

# 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)

iAdapt documentation built on Aug. 6, 2021, 9:08 a.m.