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_comb1 <- 0.25
prob_comb1_rr <- 1
rr_comb2 <- 0.20
prob_comb2_rr <- 1
rr_plac1 <- 0.10
prob_plac1_rr <- 1
rr_plac2 <- 0.10
prob_plac2_rr <- 1
correlation <- 0.8
cohorts_start <- 2
cohorts_max <- 5
safety_prob <- 0
sharing_type <- "concurrent"
sr_drugs_pos <- 5
sr_first_pos <- FALSE
n_fin <- 100
stage_data <- TRUE
cohort_random <- 0.01
cohort_offset <- 0
cohorts_sim <- Inf
random_type <- "absolute"
missing_prob <- 0.2
cohort_fixed <- 5
hist_lag <- 48
analysis_times <- c(0.5, 0.75, 1)
accrual_type <- "fixed"
accrual_param <- 9
time_trend <- 0.001
composite <- "or"
# Comparison IA1
Bayes_Sup11 <- matrix(nrow = 2, ncol = 2)
Bayes_Sup11[1,] <- c(0.00, 0.95)
Bayes_Sup11[2,] <- c(0.10, 0.80)
# Comparison IA2
Bayes_Sup12 <- matrix(nrow = 2, ncol = 2)
Bayes_Sup12[1,] <- c(0.00, 0.95)
Bayes_Sup12[2,] <- c(0.10, 0.80)
# Comparison IA3
Bayes_Sup13 <- matrix(nrow = 2, ncol = 2)
Bayes_Sup13[1,] <- c(0.00, 0.95)
Bayes_Sup13[2,] <- c(0.10, 0.80)
Bayes_Sup1 <- Bayes_Sup2 <- list(list(Bayes_Sup11), list(Bayes_Sup12), list(Bayes_Sup13))
ocs <- trial_ocs(
n_fin = n_fin, random_type = random_type, composite = composite,
rr_comb1 = rr_comb1, rr_comb2 = rr_comb2, rr_plac1 = rr_plac1, rr_plac2 = rr_plac2,
random = random, prob_comb1_rr = prob_comb1_rr, prob_comb2_rr = prob_comb2_rr,
prob_plac1_rr = prob_plac1_rr, prob_plac2_rr = prob_plac2_rr,
stage_data = stage_data, cohort_random = cohort_random, cohorts_max = cohorts_max,
sr_drugs_pos = sr_drugs_pos, sharing_type = sharing_type, correlation = correlation,
safety_prob = safety_prob, Bayes_Sup1 = Bayes_Sup1, Bayes_Sup2 = Bayes_Sup2,
cohort_offset = cohort_offset, sr_first_pos = sr_first_pos,
missing_prob = missing_prob, cohort_fixed = cohort_fixed, accrual_type = accrual_type,
accrual_param = accrual_param, hist_lag = hist_lag, analysis_times = analysis_times,
time_trend = time_trend, cohorts_start = cohorts_start, cohorts_sim = cohorts_sim,
iter = 2, coresnum = 1, save = FALSE, ret_list = TRUE, plot_ocs = TRUE
)