Description Usage Arguments Value References Examples
View source: R/operating_characteristics.R
Generic operating characteristics for one or more scenarios in EWOC simulations.
1 |
sim_list |
a list of 'ewoc_simulation' objects for different scenarios
created using the |
pdlt_list |
a list of functions to calculate the probability of toxicity with a numeric vector of doses as input and a numeric vector of probabilities as output. |
mtd_list |
a list of numerical values indicating the true MTD for each scenario. |
toxicity_margin |
a numerical value of the acceptable margin of distance from the
|
mtd_margin |
a numerical value of the acceptable margin of distance from the
|
dlt_rate
See dlt_rate
.
dose_toxicity
See optimal_toxicity
.
mtd_toxicity
See optimal_toxicity
.
statistics
See mtd_bias
and mtd_mse
.
dose_efficiency
See optimal_mtd
.
mtd_efficiency
See optimal_mtd
.
stop
See stop_rule
.
Diniz, M. A., Tighiouart, M., & Rogatko, A. (2019). Comparison between continuous and discrete doses for model based designs in cancer dose finding. PloS one, 14(1).
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### Only one simulation
DLT <- 0
dose <- 20
step_zero <- ewoc_d1classical(DLT ~ dose, type = 'discrete',
theta = 0.33, alpha = 0.25,
min_dose = 20, max_dose = 100,
dose_set = seq(20, 100, 20),
rho_prior = matrix(1, ncol = 2, nrow = 1),
mtd_prior = matrix(1, ncol = 2, nrow = 1),
rounding = "nearest")
response_sim <- response_d1classical(rho = 0.05, mtd = 60, theta = 0.33,
min_dose = 20, max_dose = 100)
sim <- ewoc_simulation(step_zero = step_zero,
n_sim = 1, sample_size = 30, n_cohort = 1,
alpha_strategy = "conditional",
response_sim = response_sim,
fixed_first_cohort = TRUE,
ncores = 1)
pdlt <- pdlt_d1classical(rho = 0.05, mtd = 60, theta = 0.33,
min_dose = 20, max_dose = 100)
opc(sim_list = list(sim), pdlt_list = list(pdlt),
mtd_list = list(60), toxicity_margin = 0.05, mtd_margin = 6)
### Two or more simulations
sim_list <- list()
mtd_list <- list()
pdlt_list <- list()
DLT <- 0
dose <- 20
step_zero <- ewoc_d1classical(DLT ~ dose, type = 'discrete',
theta = 0.33, alpha = 0.25,
min_dose = 20, max_dose = 100,
dose_set = seq(20, 100, 20),
rho_prior = matrix(1, ncol = 2, nrow = 1),
mtd_prior = matrix(1, ncol = 2, nrow = 1),
rounding = "nearest")
mtd_list[[1]] <- 60
response_sim <- response_d1classical(rho = 0.05, mtd = mtd_list[[1]],
theta = 0.33,
min_dose = 20, max_dose = 100)
sim_list[[1]] <- ewoc_simulation(step_zero = step_zero,
n_sim = 1, sample_size = 30, n_cohort = 1,
alpha_strategy = "conditional",
response_sim = response_sim,
fixed_first_cohort = TRUE,
ncores = 1)
pdlt_list[[1]] <- pdlt_d1classical(rho = 0.05, mtd = mtd_list[[1]],
theta = 0.33,
min_dose = 20, max_dose = 100)
mtd_list[[2]] <- 40
response_sim <- response_d1classical(rho = 0.05, mtd = mtd_list[[2]],
theta = 0.33,
min_dose = 20, max_dose = 100)
sim_list[[2]] <- ewoc_simulation(step_zero = step_zero,
n_sim = 1, sample_size = 30, n_cohort = 1,
alpha_strategy = "conditional",
response_sim = response_sim,
fixed_first_cohort = TRUE,
ncores = 1)
pdlt_list[[2]] <- pdlt_d1classical(rho = 0.05, mtd = mtd_list[[2]],
theta = 0.33,
min_dose = 20, max_dose = 100)
opc(sim_list = sim_list, pdlt_list = pdlt_list,
mtd_list = mtd_list, toxicity_margin = 0.05, mtd_margin = 6)
## End(Not run)
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