The goal of BayesianPlatformDesignTimeTrend is to simulates the multi-arm multi-stage or platform trial with Bayesian approach using the ‘rstan’ package, which provides the R interface for to the stan. The package uses Thall’s and Trippa’s randomisation approach for Bayesian adaptive randomisation. In addition, the time trend problem of platform trial can be studied in this package. There is a demo for multi-arm multi-stage trial for two different null scenario in this package.
You can install the ‘BayesianPlatformDesignTimeTrend’ package 1.1.1 like so:
# install.packages("BayesianPlatformDesignTimeTrend")
# devtools::install_github("ZXW834/PlatFormDesignTime", build_vignettes = TRUE)Demo_CutoffScreening() is a demo function performing
cutoff screening processDemo_multiplescrenariotrialsimulation() is a demo
function performing MAMS trial simulationMAMS-CutoffScreening-tutorial is a tutorial document of
how to do cutoff screening under Bayesian MAMS trialMAMS-trial-simulation-tutorial is a tutorial document
of how to do Bayesian MAMS trial simulation with or without time
trendThis is a basic example which shows you how to solve a common problem:
# library(BayesianPlatformDesignTimeTrend)
## basic example codeoutput=Trial.simulation(ntrials = 10000,
trial.fun = simulatetrial,
input.info = list(
response.probs = c(0.4, 0.4),
ns = c(60, 120, 180, 240, 300),
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 = Stopboundinf(
Stop.type = "Early-Pocock",
Boundary.type = "Symmetric",
cutoff = c(0.9925, 0.0075)
),
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)Here is the operational characteristics table for previous single null scenario simulation.
output$OPC#> $OPC
#> Type.I.Error.or.Power Bias rMSE N.per.arm.1
#>0404TimeTrend00stage5main 0.0444 0.0007538 0.3390904 146.4978
#> N.per.arm.2 Survive.per.arm.1 Survive.per.arm.2 N
#>0404TimeTrend00stage5main 146.7282 58.552 58.6241 293.226