trial_ocs: Calculates the operating characteristics of the cohort trial

In el-meyer/CohortPlat: Simulation of Cohort Platform Trials for Combination Treatments

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_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]]

el-meyer/CohortPlat documentation built on Jan. 29, 2024, 4:48 p.m.