trial_ocs.RdGiven the trial specific design parameters, performs a number of simulations of the trial and saves the result in an Excel file
trial_ocs(
iter,
coresnum = 1,
save = FALSE,
path = NULL,
filename = NULL,
ret_list = FALSE,
ret_trials = FALSE,
plot_ocs = FALSE,
export = NULL,
...
)Number of program simulations that should be performed
How many cores should be used for parallel computing
Indicator whether simulation results should be saved in an Excel file
Path to which simulation results will be saved; if NULL, then save to current path
Filename of saved Excel file with results; if NULL, then name will contain design parameters
Indicator whether function should return list of results
Indicator whether individual trial results should be saved as well
Should OCs stability plots be drawn?
Should any other variables be exported to the parallel tasks?
All other design parameters for chosen program
List containing: Responses and patients on experimental and control arm, total treatment successes and failures and final p-value
random <- TRUE
rr_comb <- c(0.40, 0.45, 0.50)
prob_comb_rr <- c(0.4, 0.4, 0.2)
rr_mono <- c(0.20, 0.25, 0.30)
prob_mono_rr <- c(0.2, 0.4, 0.4)
rr_back <- c(0.20, 0.25, 0.30)
prob_back_rr <- c(0.2, 0.4, 0.4)
rr_plac <- c(0.10, 0.12, 0.14)
prob_plac_rr <- c(0.25, 0.5, 0.25)
rr_transform <- list(
function(x) {return(c(0.75*(1 - x), (1-0.75)*(1-x), (1-0.75)*x, 0.75*x))},
function(x) {return(c(0.85*(1 - x), (1-0.85)*(1-x), (1-0.85)*x, 0.85*x))}
)
prob_rr_transform <- c(0.5, 0.5)
cohorts_max <- 4
safety_prob <- 0
sharing_type <- "all"
trial_struc <- "all_plac"
sr_drugs_pos <- 4
n_int <- 100
n_fin <- 200
stage_data <- TRUE
cohort_random <- 0.05
target_rr <- c(0,0,1)
cohort_offset <- 0
random_type <- "absolute"
sr_first_pos <- FALSE
missing_prob <- 0.1
# Vergleich Combo vs Mono
Bayes_Sup1 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup1[1,] <- c(0.05, 0.90, 1.00)
Bayes_Sup1[2,] <- c(0.05, 0.65, 1.00)
Bayes_Sup1[3,] <- c(0.10, 0.50, 1.00)
# Vergleich Combo vs Backbone
Bayes_Sup2 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup2[1,] <- c(0.05, 0.90, 1.00)
Bayes_Sup2[2,] <- c(NA, NA, NA)
Bayes_Sup2[3,] <- c(NA, NA, NA)
# Vergleich Mono vs Placebo
Bayes_Sup3 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup3[1,] <- c(0.00, 0.90, 1.00)
Bayes_Sup3[2,] <- c(NA, NA, NA)
Bayes_Sup3[3,] <- c(NA, NA, NA)
# Vergleich Back vs Placebo
Bayes_Sup4 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup4[1,] <- c(0.00, 0.90, 1.00)
Bayes_Sup4[2,] <- c(NA, NA, NA)
Bayes_Sup4[3,] <- c(NA, NA, NA)
Bayes_Sup <- list(list(Bayes_Sup1, Bayes_Sup2, Bayes_Sup3, Bayes_Sup4),
list(Bayes_Sup1, Bayes_Sup2, Bayes_Sup3, Bayes_Sup4))
ocs <- trial_ocs(
n_int = n_int, n_fin = n_fin, random_type = random_type,
rr_comb = rr_comb, rr_mono = rr_mono, rr_back = rr_back, rr_plac = rr_plac,
rr_transform = rr_transform, random = random, prob_comb_rr = prob_comb_rr,
prob_mono_rr = prob_mono_rr, prob_back_rr = prob_back_rr, prob_plac_rr = prob_plac_rr,
stage_data = stage_data, cohort_random = cohort_random, cohorts_max = cohorts_max,
sr_drugs_pos = sr_drugs_pos, target_rr = target_rr, sharing_type = sharing_type,
sr_first_pos = sr_first_pos, safety_prob = safety_prob, Bayes_Sup = Bayes_Sup,
prob_rr_transform = prob_rr_transform, cohort_offset = cohort_offset,
trial_struc = trial_struc, missing_prob = missing_prob,
iter = 10, coresnum = 1, save = FALSE, ret_list = TRUE, plot_ocs = TRUE
)
ocs[[3]]