simulate_trial: Simulates the cohort trial.

Description Usage Arguments Value Examples

View source: R/simulate_trial.R

Description

Simulates the cohort trial.

Usage

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simulate_trial(
  n_int = 50,
  n_fin = 100,
  cohorts_start = 1,
  rr_comb,
  rr_mono,
  rr_back,
  rr_plac,
  rr_transform,
  random_type = NULL,
  trial_struc = "all_plac",
  random = FALSE,
  prob_comb_rr = NULL,
  prob_mono_rr = NULL,
  prob_back_rr = NULL,
  prob_plac_rr = NULL,
  prob_rr_transform = prob_rr_transform,
  stage_data = TRUE,
  cohort_random = NULL,
  cohorts_max = 4,
  sr_drugs_pos = 1,
  sr_pats = cohorts_max * (n_fin + 3 * cohorts_max),
  sr_first_pos = FALSE,
  target_rr = c(0, 0, 1),
  cohort_offset = 0,
  sharing_type = "all",
  safety_prob = 0,
  ...
)

Arguments

n_int

Sample size per cohort to conduct interim analysis

n_fin

Sample size per cohort at final

cohorts_start

Number of cohorts to start the platform with

rr_comb

Response rates of combination therapies

rr_mono

Response rate of mono therapies

rr_back

Response rates of backbone arms

rr_plac

Response rate of the placebo

rr_transform

Function transforming all the above response rates to a vector of four probabilities for the multinomial simulation First element is probability of both failures. Second element is probability of biomarker success and histology failure. Third element is probability of biomarker failure and histology success. Fourth element is probability of both success.

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".

trial_struc

Trial Structure: "all_plac" = all cohorts have placebo arm

"no_plac" = no cohort has placebo arm

"stop_post_mono" = all cohorts start with placebo arm, but after first mono has been declared successful, newly enrolled cohorts have no more placebo

"stop_post_back" = all cohorts start with placebo arm, but after first backbone has been declared successful, newly enrolled cohorts have no more placebo

random

Should the response rates of the arms be randomly drawn from rr_exp? Default is FALSE.

prob_comb_rr

If random == TRUE, what are the probabilities with which the elements of rr_comb should be drawn?

prob_mono_rr

If random == TRUE, what are the probabilities with which the elements of rr_mono should be drawn?

prob_back_rr

If random == TRUE, what are the probabilities with which the elements of rr_back should be drawn?

prob_plac_rr

If random == TRUE, what are the probabilities with which the elements of rr_plac should be drawn?

prob_rr_transform

If random == TRUE, what are the probabilities with which the elements of rr_transform 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 patient, 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.

target_rr

What is target to declare a combo a positive? Vector of length 3 giving 1) the threshold by which the combo needs to be better than the monos and 2) the threhsold by which the monos need to be better than the placebo. The third element of the vector specifies the relation, choices are 1=="risk-difference", 2=="risk-ratio" and 3=="odds-ratio". By default: c(0,0, "risk-difference").

cohort_offset

Minimum number of patients 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 safety stop after every patient

...

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

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random <- TRUE

rr_comb <- c(0.25, 0.35, 0.4)
prob_comb_rr <- c(0.4, 0.4, 0.2)
rr_mono <- c(0.15, 0.20, 0.25)
prob_mono_rr <- c(0.2, 0.4, 0.4)
rr_back <- c(0.20, 0.25, 0.30)
prob_back_rr <- c(0.3, 0.4, 0.3)
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
trial_struc <- "stop_post_back"
safety_prob <- 0
sharing_type <- "concurrent"
sr_drugs_pos <- 4
sr_first_pos <- TRUE
n_int <- 50
n_fin <- 100
stage_data <- TRUE
cohort_random <- 0.05
target_rr <- c(0,0,1)
cohort_offset <- 5
random_type <- "risk_difference"

# Vergleich Combo vs Mono
Bayes_Sup1 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup1[1,] <- c(0.00, 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, 0.80, 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(0.05, 0.65, 1.00)
Bayes_Sup3[3,] <- c(NA, NA, NA)
Bayes_Sup4 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup4[1,] <- c(0.00, 0.90, 1.00)
Bayes_Sup4[2,] <- c(0.05, 0.65, 1.00)
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))

# Vergleich Combo vs Mono
Bayes_Fut1 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut1[1,] <- c(0.00, 0.60)
# Vergleich Combo vs Backbone
Bayes_Fut2 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut2[1,] <- c(0.00, 0.60)
# Vergleich Mono vs Placebo
Bayes_Fut3 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut3[1,] <- c(0.00, 0.60)
Bayes_Fut4 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut4[1,] <- c(0.00, 0.60)
Bayes_Fut <- list(list(Bayes_Fut1, Bayes_Fut2, Bayes_Fut3, Bayes_Fut4),
                  list(Bayes_Fut1, Bayes_Fut2, Bayes_Fut3, Bayes_Fut4))

a <- simulate_trial(
n_int = n_int, n_fin = n_fin, trial_struc = trial_struc, 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,
safety_prob = safety_prob, Bayes_Sup = Bayes_Sup, prob_rr_transform = prob_rr_transform,
cohort_offset = cohort_offset, sr_first_pos = sr_first_pos, Bayes_Fut = Bayes_Fut
)

CohortPlat documentation built on May 17, 2021, 5:07 p.m.