simulatetrial {BayesianPlatformDesignTimeTrend}

simulatetrial

Description

This function simulates a MAMS trial applying adaptive methods where the time trend effect can be studied.

Usage

simulatetrial(
  ii,
  response.probs = c(0.4, 0.4),
  ns = c(30, 60, 90, 120, 150),
  test.type = "Twoside",
  max.ar = 0.75,
  rand.algo = "Urn",
  max.deviation = 3,
  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")),
  Stopbound.inf = Stopbound.inf,
  Random.inf = Random.inf,
  trend.inf = trend.inf
)

Arguments

ii

Meaning less parameter but required for foreach function in doParallel package

response.probs

A vector of true response probability for each arm. Default response.probs = c(0.4, 0.4).

ns

A vector of accumulated number of patient at each stage. Default is ns = c(30, 60, 90, 120, 150).

test.type

A indicator of whether to use one side test or two side test for each treatment-control comparison.

max.ar

The upper boundary for randomisation ratio for each arm. Default is 0.75 for a two arm trial. The minimum value depends on K where 1 - max.ar <= 1/K

rand.algo

The method of applying patient allocation with a given randomisation probability vector. Default is "Urn".

max.deviation

The tuning parameter for Urn randomisation method. Default is 3.

model.inf

The list of interim data analysis model information for more see modelinf.fun

Stopbound.inf

The list of stop boundary information for more see Stopboundinf

Random.inf

The list of Adaptive randomisation information for more see Randomisation.inf

trend.inf

The list of time trend information

Value

A matrix including all evaluation metrics

Author(s)

Ziyan Wang

Examples

set.seed(1)
simulatetrial(response.probs = c(0.4, 0.4),
               ns = c(30, 60, 90, 120, 150),
               max.ar = 0.75,
               test.type = "Twoside",
               rand.algo = "Urn",
               max.deviation = 3,
               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"
               )
               ),
               Stopbound.inf = Stopboundinf(
               Stop.type = "Early-Pocock",
               Boundary.type = "Symmetric",
               cutoff = c(0.99,0.01)
               ),
               Random.inf = list(
               Fixratio = FALSE,
               Fixratiocontrol = NA,
               BARmethod = "Thall",
               Thall.tuning.inf = list(tuningparameter = "Fixed",  fixvalue = 1),
               Trippa.tuning.inf = list(a = 10, b = 0.75)
               ),
               trend.inf = list(
               trend.type = "step",
               trend.effect = c(0, 0),
               trend_add_or_multip = "mult"
               ))