opc function

Operating characteristics for EWOC simulations

Generic operating characteristics for one or more scenarios in EWOC simulations.

opc(sim_list, pdlt_list, mtd_list, toxicity_margin = NULL, mtd_margin = NULL)

Arguments

• sim_list: a list of 'ewoc_simulation' objects for different scenarios created using the ewoc_simulation function.
• 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 target_rate.
• mtd_margin: a numerical value of the acceptable margin of distance from the mtd_list.

Returns

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.

Examples

## Not run:

### 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)

References

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).