# Simulates the cohort trial. ```r simulate_trial( n_fin, cohorts_start = 1, composite = "or", rr_comb1, rr_plac1, rr_comb2, rr_plac2, random_type = NULL, random = FALSE, correlation, prob_comb1_rr = NULL, prob_plac1_rr = NULL, prob_comb2_rr = NULL, prob_plac2_rr = NULL, stage_data = FALSE, cohort_random = NULL, cohorts_max = 4, sr_drugs_pos = 1, sr_pats = cohorts_max * (n_fin + 3 * cohorts_max), sr_first_pos = FALSE, cohort_offset = 0, sharing_type = "all", safety_prob = 0, cohorts_sim = Inf, missing_prob = 0, cohort_fixed = NULL, accrual_type = "fixed", accrual_param = 9, hist_lag = 48, analysis_times = c(0.5, 0.75, 1), time_trend = time_trend, ... ) ``` ## Arguments - `n_fin`: Sample size per cohort at final - `cohorts_start`: Number of cohorts to start the platform with - `composite`: Rule for deriving the composite endpoint. By default "or", otherwise "and" - `rr_comb1`: Response rates of treatment, histology endpoint 1 - `rr_plac1`: Response rate of the SoC, histology endpoint 1 - `rr_comb2`: Response rates of treatment, histology endpoint 2 - `rr_plac2`: Response rate of the SoC, histology endpoint 2 - `random_type`: How should the response rates be drawn randomly? Options are: "absolute": Specify absolute response rates that will be drawn with a certain probability "risk_difference": Specify absolute response rates for placebo which will be drawn randomly, plus specify vectors for absolute treatment effects of mono therapies over placebo and for combo over the mono therapies. "risk_ratio": Specify absolute response rates for placebo which will be drawn randomly, plus specify vectors for relative treatment effects of mono therapies over placebo and for combo over the mono therapies. "odds_ratios": Specify response rate for placebo, specify odds-ratios for mono therapies (via rr_back and rr_mono) and respective probabilities. On top, specify interaction for the combination therapy via rr_comb with prob_rr_comb. Set: odds_combo = odds_plac * or_mono1 * or_mono2 * rr_comb. If rr_comb > 1 -> synergistic, if rr_comb = 1 -> additive. If rr_comb < 1 -> antagonistic. Default is "NULL". - `random`: Should the response rates of the arms be randomly drawn from rr_exp? Default is FALSE. - `correlation`: Correlation between histology endpoints - `prob_comb1_rr`: If random == TRUE, what are the probabilities with which the elements of rr_comb1 should be drawn? - `prob_plac1_rr`: If random == TRUE, what are the probabilities with which the elements of rr_plac1 should be drawn? - `prob_comb2_rr`: If random == TRUE, what are the probabilities with which the elements of rr_comb2 should be drawn? - `prob_plac2_rr`: If random == TRUE, what are the probabilities with which the elements of rr_plac2 should be drawn? - `stage_data`: Should individual stage data be passed along? Default is TRUE - `cohort_random`: If not NULL, indicates that new arms/cohorts should be randomly started. For every timestep, there is a cohort_random probability that a new cohort will be started. - `cohorts_max`: Maximum number of cohorts that are allowed to be added throughout the trial - `sr_drugs_pos`: Stopping rule for successful experimental arms; Default = 1 - `sr_pats`: Stopping rule for total number of patients; Default = cohorts_max * n_fin + error term based on randomization - `sr_first_pos`: Stopping rule for first successful cohort; if TRUE, after first cohort was found to be successful, no further cohorts will be included but cohorts will finish evaluating, unless other stopping rules reached prior. Default is FALSE. - `cohort_offset`: Minimum number of time between adding new cohorts - `sharing_type`: Which backbone and placebo data should be used for arm comparisons; Default is "all". Another option is "concurrent" or "dynamic" or "cohort". - `safety_prob`: Probability for a random stopping after every patient - `cohorts_sim`: Maximum number of cohorts that can run simultaneously - `missing_prob`: Probability for a missing value at final (independent of treatment) - `cohort_fixed`: If not NULL, fixed timesteps after which a cohort will be included - `accrual_type`: Type of patient accrual; choices are "fixed", "poisson" or "exponential" - `accrual_param`: Parameter used for patient accrual - `hist_lag`: Time until histology outcome is observed - `analysis_times`: Vector of information fractions needed for first interim, second interim and final - `time_trend`: Additive term by which response rates increase at every time step - `...`: Further arguments to be passed to decision function, such as decision making criteria ## Returns List containing: Responses and patients on experimental and control arm, total treatment successes and failures and final p-value ## Examples ```r random <- TRUE rr_comb1 <- 0.10 prob_comb1_rr <- 1 rr_comb2 <- 0.45 prob_comb2_rr <- 1 rr_plac1 <- 0.10 prob_plac1_rr <- 1 rr_plac2 <- 0.20 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)) # Comparison IA1 Bayes_Fut11 <- matrix(nrow = 1, ncol = 2) Bayes_Fut11[1,] <- c(0.00, 0.20) # Comparison IA2 Bayes_Fut12 <- matrix(nrow = 1, ncol = 2) Bayes_Fut12[1,] <- c(0.00, 0.30) # Comparison IA3 Bayes_Fut13 <- matrix(nrow = 1, ncol = 2) Bayes_Fut13[1,] <- c(NA, NA) # Endpoint 1+2 Bayes_Fut1 <- Bayes_Fut2 <- list(list(Bayes_Fut11), list(Bayes_Fut12), list(Bayes_Fut13)) simulate_trial( 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, correlation = correlation, stage_data = stage_data, cohort_random = cohort_random, cohorts_max = cohorts_max, sr_drugs_pos = sr_drugs_pos, sharing_type = sharing_type, Bayes_Fut1 = Bayes_Fut1, safety_prob = safety_prob, Bayes_Sup1 = Bayes_Sup1, Bayes_Sup2 = Bayes_Sup2, cohort_offset = cohort_offset, sr_first_pos = sr_first_pos, Bayes_Fut2 = Bayes_Fut2, 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 ) ```

simulate_trial function