Package 'cats' reference manual

Title:	Cohort Platform Trial Simulation
Description:	Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated.
Authors:	Elias Laurin Meyer [aut, cre], Peter Mesenbrink [ctb], Franz Koenig [ctb]
Maintainer:	Elias Laurin Meyer <[email protected]>
License:	GPL-3
Version:	1.0.3
Built:	2025-02-24 06:19:56 UTC
Source:	https://github.com/el-meyer/cats

Checks whether decision criteria are met and updates trial results accordingly.

Description

Given a res_list object, checks the supplied decision criteria and saves the results in the res_list file.

Usage

make_decision_trial(
  res_list,
  which_cohort,
  Bayes_Sup1 = NULL,
  Bayes_Fut1 = NULL,
  Bayes_Sup2 = NULL,
  Bayes_Fut2 = NULL,
  w = 0.5,
  analysis_number,
  beta_prior = 0.5,
  hist_lag,
  endpoint_number,
  analysis_time,
  dataset,
  hist_miss = TRUE,
  sharing_type,
  ...
)
make_decision_trial(
  res_list,
  which_cohort,
  Bayes_Sup1 = NULL,
  Bayes_Fut1 = NULL,
  Bayes_Sup2 = NULL,
  Bayes_Fut2 = NULL,
  w = 0.5,
  analysis_number,
  beta_prior = 0.5,
  hist_lag,
  endpoint_number,
  analysis_time,
  dataset,
  hist_miss = TRUE,
  sharing_type,
  ...
)

Arguments

`res_list`	List item containing individual cohort trial results so far in a format used by the other functions in this package
`which_cohort`	Current cohort that should be evaluated
`Bayes_Sup1`	List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for superiority of histology endpoint 1
`Bayes_Fut1`	List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for futility of histology endpoint 1
`Bayes_Sup2`	List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for superiority of histology endpoint 2
`Bayes_Fut2`	List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for futility of histology endpoint 2
`w`	If dynamic borrowing, what is the prior choice for w. Default is 0.5.
`analysis_number`	1st, second or third analysis?
`beta_prior`	Prior parameter for all Beta Distributions. Default is 0.5.
`hist_lag`	Histology Lag
`endpoint_number`	Should histology endpoint 1 or 2 be evaluated?
`analysis_time`	Platform Time of Analysis
`dataset`	Dataset to be used for analysis
`hist_miss`	Whether or not to exclude missing histology data
`sharing_type`	Type of Data Sharing to perform
`...`	Further arguments inherited from simulate_trial

Value

List containing original res_list and results of decision rules

Examples


# Example 1

# Initialize empty data frame
cols <- c("PatID", "ArrivalTime", "Cohort", "Arm", "RespHist1", "RespHist2", "HistMissing")
df <- matrix(nrow = 100, ncol = length(cols))
colnames(df) <- cols
df <- as.data.frame(df)
df$PatID <- 1:100
df$ArrivalTime <- sort(runif(100, min = 0, max = 5))
df$Cohort <- sample(1:2, 100, replace = TRUE)
df$Arm <- sample(c("Combo", "Plac"), 100, replace = TRUE)
df$RespHist1 <- sample(0:1, 100, replace = TRUE)
df$RespHist2 <- sample(0:1, 100, replace = TRUE)
df$HistMissing <- sample(0:1, 100, replace = TRUE, prob = c(0.95, 0.05))

# Initialize res_list Object

res_list <-
rep(
  list(
    list(
      Meta = list(
        decision = rep("none", 3),
        decision_hist1 = rep("none", 3),
        decision_hist2 = rep("none", 3),
        start_n = 0,
        start_time = 0,
        pat_enrolled = 0
      ),
      Arms = rep(
        list(
          list(
            rr = NULL,
            hist_observed = 0
          )
        ),
        2
      )
    )
  ),
  2
)

arm_names <- c("Comb", "Plac")

for (i in 1:2) {
  names(res_list)[i] <- paste0("Cohort", i)
  names(res_list[[i]]$Arms) <- arm_names

  res_list[[i]]$Arms$Comb$rr <- matrix(c(0.2, 0.2), ncol = 2)
  res_list[[i]]$Arms$Plac$rr <- matrix(c(0.1, 0.1), ncol = 2)
}

sharing_type <- "all"
analysis_number <- 3
which_cohort <- 1
endpoint_number <- 2
hist_lag <- 1
analysis_time <- 6

# 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(NA, NA)
# 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))


# DO NOT RUN
res_list2 <-
make_decision_trial(
res_list = res_list, which_cohort = which_cohort,
analysis_number = analysis_number, endpoint_number = endpoint_number,
Bayes_Sup1 = Bayes_Sup1, Bayes_Sup2 = Bayes_Sup2,
dataset = df, analysis_time = analysis_time, hist_lag = hist_lag,
sharing_type = sharing_type
)

# Example 1

# Initialize empty data frame
cols <- c("PatID", "ArrivalTime", "Cohort", "Arm", "RespHist1", "RespHist2", "HistMissing")
df <- matrix(nrow = 100, ncol = length(cols))
colnames(df) <- cols
df <- as.data.frame(df)
df$PatID <- 1:100
df$ArrivalTime <- sort(runif(100, min = 0, max = 5))
df$Cohort <- sample(1:2, 100, replace = TRUE)
df$Arm <- sample(c("Combo", "Plac"), 100, replace = TRUE)
df$RespHist1 <- sample(0:1, 100, replace = TRUE)
df$RespHist2 <- sample(0:1, 100, replace = TRUE)
df$HistMissing <- sample(0:1, 100, replace = TRUE, prob = c(0.95, 0.05))

# Initialize res_list Object

res_list <-
rep(
  list(
    list(
      Meta = list(
        decision = rep("none", 3),
        decision_hist1 = rep("none", 3),
        decision_hist2 = rep("none", 3),
        start_n = 0,
        start_time = 0,
        pat_enrolled = 0
      ),
      Arms = rep(
        list(
          list(
            rr = NULL,
            hist_observed = 0
          )
        ),
        2
      )
    )
  ),
  2
)

arm_names <- c("Comb", "Plac")

for (i in 1:2) {
  names(res_list)[i] <- paste0("Cohort", i)
  names(res_list[[i]]$Arms) <- arm_names

  res_list[[i]]$Arms$Comb$rr <- matrix(c(0.2, 0.2), ncol = 2)
  res_list[[i]]$Arms$Plac$rr <- matrix(c(0.1, 0.1), ncol = 2)
}

sharing_type <- "all"
analysis_number <- 3
which_cohort <- 1
endpoint_number <- 2
hist_lag <- 1
analysis_time <- 6

# 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(NA, NA)
# 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))


# DO NOT RUN
res_list2 <-
make_decision_trial(
res_list = res_list, which_cohort = which_cohort,
analysis_number = analysis_number, endpoint_number = endpoint_number,
Bayes_Sup1 = Bayes_Sup1, Bayes_Sup2 = Bayes_Sup2,
dataset = df, analysis_time = analysis_time, hist_lag = hist_lag,
sharing_type = sharing_type
)

Simulates the cohort trial.

Description

Simulates the cohort trial.

Usage

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,
  ...
)
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

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 <- 0.10
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
)

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
)

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,
  ...
)
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
)

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
)

Package 'cats'

Help Index

Checks whether decision criteria are met and updates trial results accordingly.

Description

Usage

Arguments

Value

Examples

Simulates the cohort trial.

Description

Usage

Arguments

Value

Examples

Calculates the operating characteristics of the cohort trial

Description

Usage

Arguments

Value

Examples