platform_midas2s {midas2}

An Bayesian platform design without subgroup efficacy exploration(midas-2s), which is the degenerate competing design in the simulation.

Description

MIDAS-2s is the degenerate competing designs that do not consider subgroups. Beta-binomial model is applied for efficacy in whole population of each arm.

Usage

platform_midas2s(seed, p, p_tox, C_T = 0.85, C_E1 = 0.15, C_E2 = 0.999)

Arguments

seed

set a random seed to maintain the repeatability of the simulation results.

p

a matrix indicating the efficacy. Row number represents the number of candidate drugs.

p_tox

a vector indicating the toxicity.

C_T

early toxicity stopping threshold, which refers to a predefined threshold used to determine when a clinical trial should be stopped early due to unacceptable levels of toxicity or adverse events in the study participants. This threshold is established to ensure the safety and well-being of the trial participants and to prevent further harm.

C_E1

early futility stopping threshold, which refers to a predefined threshold used to determine when a clinical trial should be stopped early due to lack of efficacy or futility. It is established to prevent the continuation of a trial that is unlikely to demonstrate a significant treatment effect, thus saving time, resources, and participant exposure to ineffective treatments.

C_E2

early efficacy stopping threshold, which refers to a predefined threshold used to determine when a clinical trial should be stopped early due to the demonstration of significant efficacy or positive treatment effects. This threshold is established to allow for timely decision-making and saves sample size.

Value

term.tox the indicator of whether early stopping for toxicity

term.fut the indicator of whether early stopping for futility

term.eff the indicator of whether early stopping for efficacy

final.eff a vector of final decision, either efficacy or inefficacy

N sample size, which refers to the number of participants included in a study or experiment.

Examples

  # Example 1
  p0 <- c(0.1,0.1,0.1,0.1)
  p1 <- c(0.1,0.1,0.1,0.1)

  p <- rbind(p0,p1)
  p_tox <- c(0.1,0.4)

  # consider 1 candidate drugs with 4 subgroups
  result <- platform_midas2s(seed=20,p,p_tox,C_T=0.85,C_E1=0.15,C_E2=0.999)
  result


  
  # Example 2
  p0 <- c(0.05,0.10,0.05,0.10)
  p1 <- c(0.24,0.40,0.12,0.22)
  p2 <- c(0.24,0.40,0.12,0.22)
  p3 <- c(0.12,0.22,0.05,0.10)
  p4 <- c(0.24,0.40,0.12,0.22)
  p5 <- c(0.28,0.45,0.12,0.22)
  p6 <- c(0.24,0.40,0.12,0.22)
  p7 <- c(0.12,0.22,0.05,0.10)

  p <- rbind(p0, p1, p2, p3, p4, p5, p6, p7)
  p_tox <- c(0.10,0.10,0.10,0.10,0.10,0.10,0.15,0.20)

  # consider 7 candidate drugs with 4 subgroups
  result <- platform_midas2s(seed=12,p,p_tox,C_T=0.85,C_E1=0.15,C_E2=0.999)
  result