demo_Cutoffscreening {BayesianPlatformDesignTimeTrend}

demo_Cutoffscreening

Description

This function does a cutoff screening for trial simulation.

Usage

demo_Cutoffscreening(
  ntrials = 1000,
  trial.fun = simulatetrial,
  grid.inf = list(start = c(0.9, 0.95, 1), extendlength = 15),
  input.info = list(response.probs = c(0.4, 0.4), ns = c(30, 60, 90, 120, 150), max.ar =
    0.75, rand.algo = "Urn", max.deviation = 3, test.type = "Twoside", model.inf =
    list(model = "tlr", ibb.inf = list(pi.star = 0.5, pess = 2, betabinomialmodel =
    ibetabinomial.post), tlr.inf = list(beta0_prior_mu = 0, beta1_prior_mu = 0,
    beta0_prior_sigma = 2.5, beta1_prior_sigma = 2.5, beta0_df = 7, beta1_df = 7, reg.inf
    = "main", variable.inf = "Fixeffect")), Stop.type = "Early-Pocock", Boundary.type =
    "Symmetric", Random.inf = list(Fixratio = FALSE, 
     Fixratiocontrol = NA,
    BARmethod = "Thall", Thall.tuning.inf = list(tuningparameter = "Fixed", fixvalue =
    1)), trend.inf = list(trend.type = "step", trend.effect = c(0, 0),
    trend_add_or_multip = "mult")),
  cl = 2
)

Arguments

ntrials

A numeric variable indicating how many trial replicates you want to run

trial.fun

The function of trial simulation, related to MainFunction.R

grid.inf

A list of grid information to create start grid and extend grid for cutoff screening.

input.info

A list of input information including all information required for trial simulation.

cl

A numeric variable indicating how many cores you want to use in parallel programming.

Value

A vector of recommended cutoff. The final value is the latest recommended value. A plot for all tested cutoff and error rate

Author(s)

Ziyan Wang

Examples

demo_Cutoffscreening(ntrials = 2, cl = 2,
    grid.inf = list(start = c(0.9, 0.95, 1), extendlength = 2))