trial_ocs {cats}

Calculates the operating characteristics of the cohort trial

Description

Given the trial specific design parameters, performs a number of simulations of the trial and saves the result in an Excel file

Usage

trial_ocs(
  iter,
  coresnum = 1,
  save = FALSE,
  path = NULL,
  filename = NULL,
  ret_list = FALSE,
  ret_trials = FALSE,
  plot_ocs = FALSE,
  export = NULL,
  ...
)

Arguments

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

Value

List containing: Responses and patients on experimental and control arm, total treatment successes and failures and final p-value

Examples

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
)