trial_ocs | R Documentation |
Given 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, ... )
iter |
Number of program simulations that should be performed |
coresnum |
How many cores should be used for parallel computing |
save |
Indicator whether simulation results should be saved in an Excel file |
path |
Path to which simulation results will be saved; if NULL, then save to current path |
filename |
Filename of saved Excel file with results; if NULL, then name will contain design parameters |
ret_list |
Indicator whether function should return list of results |
ret_trials |
Indicator whether individual trial results should be saved as well |
plot_ocs |
Should OCs stability plots be drawn? |
export |
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]]
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