Package 'BayesianPlatformDesignTimeTrend'

Title: Simulate and Analyse Bayesian Platform Trial with Time Trend
Description: Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and Wathen et al. (2017) <doi:10.1177/1740774517692302>. The randomisation algorithm is described in: Zhao W <doi:10.1016/j.cct.2015.06.008>. The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) <doi:10.1177/17407745221112013> and Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.
Authors: Ziyan Wang [aut, cre], David Woods [ctb]
Maintainer: Ziyan Wang <[email protected]>
License: MIT + file LICENSE
Version: 1.2.0
Built: 2025-01-24 04:45:40 UTC
Source: https://github.com/zxw834/bayesianplatformdesigntimetrend

Help Index

The 'BayesianPlatformDesignTimeTrend' package.

Description

This package 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.

References

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org

AdaptiveRandomisation

Description

This is a function doing the randomisation process. This Function generates the Sequence for patient allocation to each arm, patient outcomes.

Usage

AdaptiveRandomisation(
  Fixratio,
  rand.algo,
  K,
  n.new,
  randomprob,
  treatmentindex,
  groupwise.response.probs,
  group,
  armleft,
  max.deviation,
  trend_add_or_multip,
  trend.function,
  trend.effect,
  ns,
  Fixratiocontrol
)

Arguments

Fixratio

A indicator TRUE/FALSE
rand.algo

Randomisation algorithm: "Coin": Biased coin; "Urn": Urn method
K

Total number of arms at the beginning
n.new

The cohort size
randomprob

A named vector of randomisation probability to each arm
treatmentindex

The vector of treatment arm index excluding the control arm whose index is 0
groupwise.response.probs

A matrix of response probability of each arm
group

The current stage
armleft

The number of treatment left in the platform (>2)
max.deviation

Tuning parameter of using urn randomisation method.
trend_add_or_multip

How time trend affects the true response probability: "add" or "mult"
trend.function

The function returns time trend effect regarding to different time trend pattern
trend.effect

The strength of time trend effect as a parameter in trend.function()
ns

A vector of accumulated number of patient at each stage
Fixratiocontrol

A numeric value indicating the weight of control in randomisation. Eg. 1 means equal randomisation, 2 means thw number of patients allocated to control is twice as large as other treatment arm.

Value

A list of patient allocation and patient outcome nstage: A vector of the number of patients allocated to each arm ystage: A vector of the patients outcome after treating with each arm znew: A vector of treatment index assigned to each patient in the current cohort ynew: A vector of outcome index record for each patient after treatment in the current cohort

Author(s)

Ziyan Wang

References

Mass weighted urn design—a new randomization algorithm for unequal allocations. Zhao, Wenle. Contemporary clinical trials 43 (2015): 209-216.

Examples

AdaptiveRandomisation(
Fixratio = FALSE,
rand.algo = "Urn",
K = 2,
n.new = 30,
randomprob = matrix(c(0.5, 0.5), ncol = 2, dimnames = list(c(),c("1","2"))),
treatmentindex = 1,
groupwise.response.probs = matrix(rep(c(0.4, 0.4), 5), byrow = TRUE, ncol = 2, nrow = 5),
group = 1,
armleft = 2,
max.deviation = 3,
trend_add_or_multip = "mult",
trend.function = function(ns, group, i, trend.effect) {delta = 0; return(delta)},
trend.effect = c(0, 0),
ns = c(30, 60, 90, 120, 150),
Fixratiocontrol = NA)

alphaspending

Description

This function estimates the mean error rate spent at each interim analysis for a trial Example usage:

  1. sapply(res = result, fun = alphaspending) will generate list of the proportion of trial replicates are stopped at each stage for all scenarios in result where result is a list containing output data for different scenario

  2. sapply(sapply(result,FUN = alphaspending),sum) will generate the type I error rate or power for all scenario on the result list

  3. alpha(result) generate the proportion of trial replicates are stopped at each stage where result is the output data for one specific scenario

  4. sum(alpha(result)) will generate the type I error rate or power for a specific scenario

Usage

alphaspending(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

The error rate at each interim analysis

Author(s)

Ziyan Wang

Examples

## Not run: alphaspending(res)

ARmethod

Description

This function adjusts the posterior randomisation probability for each arm using many approaches. Currently Thall's approach and Trippa's approach are used. Double biased coin and other method will be added in the next version.

Usage

ARmethod(
  Fixratio,
  BARmethod,
  group,
  stats,
  post.prob.btcontrol,
  K,
  n,
  tuningparameter = NA,
  c = NA,
  a = NA,
  b = NA,
  post.prob.best,
  max.ar = NA,
  armleft,
  treatmentindex
)

Arguments

Fixratio

A indicator TRUE/FALSE
BARmethod

The indicator of which adaptive randomisation method is used
group

The current stage
stats

The output matrix
post.prob.btcontrol

The vector of posterior probability of each active treatment arm better than control
K

Total number of arms at the beginning
n

The vector of sample size for each arm
tuningparameter

The tuning parameter indicator for Thall's approach
c

The tuning parameter for Thall's approach
a

The hyperparamter parameter for Trippa's approach
b

The hyperparamter parameter for Trippa's approach
post.prob.best

Posterior probability of each arm to be the best
max.ar

The upper boundary for randomisation ratio for each arm, which is used in Thall's approach since Trippa's approach has protection on control arm.
armleft

The number of treatment left in the platform (>2)
treatmentindex

The vector of treatment arm index excluding the control arm whose index is 0

Value

randomprob: The vector of adjusted randomisation probability to each arm

Author(s)

Ziyan Wang

References

Bayesian adaptive randomized trial design for patients with recurrent glioblastoma. Trippa, Lorenzo, Eudocia Q. Lee, Patrick Y. Wen, Tracy T. Batchelor, Timothy Cloughesy, Giovanni Parmigiani, and Brian M. Alexander. Journal of Clinical Oncology 30, no. 26 (2012): 3258. A simulation study of outcome adaptive randomization in multi-arm clinical trials. Wathen, J. Kyle, and Peter F. Thall. Clinical Trials 14, no. 5 (2017): 432-440.

Examples

ARmethod(Fixratio = FALSE,
BARmethod = "Thall",
group = 1,
stats = matrix(rep(NA, 40), ncol = 8, nrow = 5),
post.prob.btcontrol = 0.5,
K = 2,
n = c(30, 30),
tuningparameter = "fixed",
c = 1,
post.prob.best = c(0.5, 0.5),
max.ar = 0.75,
armleft = 2,
treatmentindex = 1)

ARmethod(Fixratio = FALSE,
BARmethod = "Trippa",
group = 1,
stats = matrix(rep(NA, 40), ncol = 8, nrow = 5),
post.prob.btcontrol = c(0.5, 0.6),
K = 3,
n = c(30, 30, 40),
tuningparameter = NA,
c = NA,
a = 3,
b = 0.75,
post.prob.best = c(0.3, 0.3, 0.4),
max.ar = NA,
armleft = 3,
treatmentindex = c(1, 2))

Boundaryconstruction

Description

This function constructs the stopping boundary based on input information

Usage

Boundaryconstruction(Stopbound.inf = Stopbound.inf, ns = ns)

Arguments

Stopbound.inf

The list of stop boundary information for more see Stopboundinf
ns

A vector of accumulated number of patient at each stage

Value

A list of the futility boundary and the efficacy boundary

Author(s)

Ziyan Wang

Examples

Stopbound.inf=list(Stop.type="Early-Pocock",Boundary.type="Symmetric",cutoff=c(0.9928,0.0072))
 ns=c(60,120,180,240,300)
 Boundaryconstruction(Stopbound.inf, ns)

conjuncativepower_or_FWER

Description

This function reads in the output matrix of a number of trial replicates to calculate the Family wise error rate or Conjunctive power

Usage

conjuncativepower_or_FWER(res, scenario, test.type)

Arguments

res

A list of output matrix of a number of trial replicates
scenario

The true scenario used to generate the res list
test.type

The indicator of whether using one side or two side testing. Please make sure that the input test.type does not conflicts to the data. Otherwise the conjunctive power calculation is wrong

Value

Family wise error rate or Conjunctive power

Examples

## Not run: conjuncativepower_or_FWER(res)

Cutoff screening example: the details of grid

Description

Details of grid including famliy wise error rate of a cutoff value, the cutoff value and the square of cutoff value for modelling and prediction

Usage

dataloginformd

Format

A data frame with 24 rows and 3 variables:

tpIE

FWER

cutoff

Cutoff value

cutoff2

Square of cutoff value

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))

A demo for cutoff screening using Bayesian optimisation

Description

This function does a cutoff screening for trial simulation using Bayesian optimisation.

Usage

demo_Cutoffscreening.GP(
  ntrials = 1000,
  trial.fun = simulatetrial,
  grid.inf = list(start.length = 10, grid.min = NULL, grid.max = NULL, confidence.level =
    0.95, grid.length = 5000, change.scale = FALSE, noise = TRUE, errorrate = 0.1,
    simulationerror = 0.01, iter.max = 15, plotornot = FALSE),
  power.type = NA,
  response.probs.alt = NA,
  input.info = list(response.probs.null = c(0.4, 0.4, 0.4, 0.4), ns = c(120, 240, 360,
    480, 600), max.ar = 0.85, 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-OBF", Boundary.type = "Symmetric", 
     Random.inf = list(Fixratio = FALSE,
    Fixratiocontrol = NA, BARmethod = "Thall", Thall.tuning.inf = list(tuningparameter =
    "Unfixed", fixvalue = 1)), trend.inf = list(trend.type = "step", trend.effect = c(0,
    0, 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. 'start.length' is the size of start grid. Default is 10. 'grid.min' A numeric value or vector (for asymmetric boundary) indicating the lower bound of the grid for screening. For asymmetric boundary, the first value is efficacy minimum value and the second value is futility minimum value. 'grid.max' A numeric value or vector (for asymmetric boundary) indicating the upper bound of the grid for screening. For asymmetric boundary, the first value is efficacy maximum value and the second value is futility maximum value. 'errorrate' refers to the target of type I error rate or family-wise error rate. Default is 0.05. User can change it to 0.1 for FWER if they think 0.05 is too conservative. The per-hypothesis type I error equals errorrate / (K-1) where (K-1) is the number of treatment arms. 'confidence.level' is a numeric value indicating the confidence level of estimate. Default is 0.95. 'grid.length' A numeric value indicating the grid resolution. Default is 5000 for symmetric boundary. For asymmetric boundary, the length of grid is 101 for both efficacy grid and futility grid. A numeric value indicating the grid resolution. Default is 5000 for symmetric boundary. For asymmetric boundary, the length of grid is 101 for both efficacy grid and futility grid. 'change.scale' is a logic value indicating whether we want to change scale when doing Gaussian process. Default is FALSE. 'noise' is a logic value indicating whether the input x is noisy. Default is TRUE. 'simulationerror' is a numeric value indicating the tolerable error for simulated type I error rate. Default is 0.01, 'iter.max' is a numeric value indicating the maximum number of evaluations. Default is 15. 'plotornot' is a logic value indicating whether the errorrate vs grid plot needed to be generated at each iteration. Default is FALSE.
power.type

A indicator of which type of power we need to optimise when tuning the cutoff value for asymmetric boundary. Default is NA (Symmetric boundary). The choice of power type is Conjunctive power ("Conjunctive") and Disconjunctive power ("Disconjunctive"). In a two arm trial design, these power type are the same.
response.probs.alt

A vector of response probability of each arm under the alternative scenario. This is used for power optimisation when tuning the cutoff values for asymmetric boundary. Default is NA.
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

#Two arm asymmetric boundary screening. Default is OBF boundary.
demo_Cutoffscreening.GP(ntrials = 2, cl = 2,
 power.type = NA,
 response.probs.alt = NA,
 grid.inf = list(
 start.length = 10,
 confidence.level = 0.95,
 grid.length = 5000,
 change.scale = FALSE,
 noise = TRUE,
 errorrate = 0.1,
 simulationerror = 0.01,
 iter.max = 15,
 plotornot = FALSE))

 #Four arm asymmetric OBF boundary screening where conjunctive power is optimised.
 demo_Cutoffscreening.GP(ntrials = 2, cl = 2,
 power.type = "Conjunctive",
 response.probs.alt = c(0.4,0.6,0.6,0.4),
 grid.inf = list(
 start.length = 10,
 confidence.level = 0.95,
 grid.length = 101,
 change.scale = FALSE,
 noise = TRUE,
 errorrate = 0.1,
 simulationerror = 0.01,
 iter.max = 15,
 plotornot = FALSE))
 input.info = list(
 response.probs.null = c(0.4,0.4,0.4,0.4),
 ns = c(120, 240, 360, 480, 600),
 max.ar = 0.85,
 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-OBF",
 Boundary.type = "Asymmetric",
 Random.inf = list(
 Fixratio = FALSE,
 Fixratiocontrol = NA,
 BARmethod = "Thall",
 Thall.tuning.inf = list(tuningparameter = "Unfixed",  fixvalue = 1)
 ),
 trend.inf = list(
 trend.type = "step",
 trend.effect = c(0, 0, 0, 0),
 trend_add_or_multip = "mult"
 )
 )

demo_multscenario

Description

This is a demo function simulating multi-arm multi-stage design with two different null scenarios where the response probability of control is 0.15 and 0.4, respectively. The clinically meaningful increment on probability scale is 0.2. The stopping boundary is the OBF. The cutoff vector in the demo is tuned to keep Type I error rate to be 0.05. The output data can be saved as .RData file

Usage

demo_multscenario(ntrials = 1000, cl = 2, save_data = FALSE)

Arguments

ntrials

A numeric value. The number of total trail replicates for each scenario.
cl

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

An indicator of whether the output data need to be saved. Default is FALSE.

Value

A list of data for plotting. One is results of trial replicates for all scenarios. The other one is a data frame containing all summarised evaluation metrics for all scenarios

Author(s)

Ziyan Wang

Examples

demo_multscenario(ntrials = 2, cl = 2, save_data = FALSE)

disconjunctivepowerfunc

Description

This function reads in the output matrix of a number of trial replicates to calculate the Family wise error rate or disconjunctive power

Usage

disconjunctivepowerfunc(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

Disconjunctive power

Examples

## Not run: disconjunctivepowerfunc(res)

GP.optim: optimiser to give the next cutoff for evaluation

Description

A function to predict the next cutoff value for evaluation.

Usage

GP.optim(
  x,
  y.t1E,
  y.pow = NA,
  ESS = NA,
  errorrate = 0.05,
  confidence.level = 0.95,
  grid.length = 1000,
  change.scale = FALSE,
  noise = T,
  grid.min,
  grid.max,
  Boundary.type = "Symmetric"
)

Arguments

x

A numeric vector of cutoff data
y.t1E

A numeric vector of type I error rate data
y.pow

A numeric vector of power data. You can input conjucntive, disconjunctive and marginal power data. Default is NA. Only used when Boundary.type == "Asymmetric"
ESS

A matrix of effective sample size. This is only called for asymmetric boundary cutoff screening. Default is NA for symmetric boundary. The first column is the ESS for different cutoff pair under the null scenario, the second column is the ESS for different cutoff pair under the alternative scenario.
errorrate

'errorrate' refers to the target of type I error rate or family-wise error rate. Default is 0.05. User can change it to 0.1 for FWER if they think 0.05 is too conservative. The per-hypothesis type I error equals errorrate / (K-1) where (K-1) is the number of treatment arms.
confidence.level

A numeric value indicating the confidence level of estimate. Default is 0.95
grid.length

A numeric value indicating the grid resolution. Default is 1000.
change.scale

A logic value indicating whether we want to change scale when doing Gaussian process. Default is FALSE.
noise

A logic value indicating whether the input x is noisy. Default is TRUE.
grid.min

A numeric value or vector (for asymmetric boundary) indicating the lower bound of the grid for screening. For asymmetric boundary, the first value is efficacy minimum value and the second value is futility minimum value.
grid.max

A numeric value or vector (for asymmetric boundary) indicating the upper bound of the grid for screening. For asymmetric boundary, the first value is efficacy maximum value and the second value is futility maximum value.
Boundary.type

A text indicating what type of boundary used. Default is "Symmetric"

Value

A list including the next cutoff value for evaluation next.cutoff and a list of predictions for screening grid.

Author(s)

Ziyan Wang

References

Surrogates: Gaussian process modeling, design, and optimization for the applied sciences. CRC press. Gramacy, R.B., 2020. Bayesian optimization for adaptive experimental design: A review. IEEE access, 8, 13937-13948. Greenhill, S., Rana, S., Gupta, S., Vellanki, P., & Venkatesh, S. (2020).

Examples

x = c(7.123968, 6.449631, 1.984406,
3.507463, 4.972510, 2.925768,
5.816682, 4.367796,
7.349160, 1.113648)
y.t1E = c(0.0396, 0.0450,
0.5116, 0.2172,
0.1040, 0.3058,
0.0592, 0.1384,
0.0296, 0.7936)
grid.min=1
grid.max=8
GP.res=GP.optim(x=x, y.t1E=y.t1E, errorrate = 0.1, grid.min = grid.min, grid.max = grid.max)
GP.res$next.cutoff

x =  data.frame(matrix(c(
0.9563408, 0.006295626,
0.9669739, 0.014395030,
0.9959410, 0.034858339,
0.9635357, 0.048435579,
0.9794314, 0.021659226,
0.9552201, 0.018442535,
0.9655374, 0.035281833,
0.9837123, 0.010656442,
0.9974910, 0.047741842,
0.9989172, 0.012982826), byrow=TRUE, ncol = 2))
y.t1E = c(0.3044, 0.2938, 0.2573, 0.4780, 0.2923, 0.3733, 0.4263, 0.1962, 0.2941, 0.1131)
y.pow = c(0.8300, 0.8239, 0.7102, 0.7291, 0.8205, 0.7984, 0.7709, 0.8418, 0.6359, 0.5609)
ESS = data.frame(matrix(c(
594.672, 560.580,
596.148, 566.328,
597.840, 590.124,
590.052, 544.800,
597.024, 574.716,
593.952, 554.580,
593.676, 554.400,
598.500, 583.896,
595.740, 590.520,
599.580, 598.644),byrow=TRUE,ncol=2))
grid.min=c(0.95,0)
grid.max=c(1,0.05)
GP.res_asy=GP.optim(x=x, y.t1E=y.t1E, y.pow=y.pow, ESS=ESS,errorrate = 0.1,
grid.min = grid.min, grid.max = grid.max, Boundary.type="Asymmetric")
GP.res_asy$next.cutoff

ibetabinomial.post

Description

This function calculates the posterior probability of each active treatment arm better than control using betabinomial model

Usage

ibetabinomial.post(n, y, pi.star = 0.5, pess = 2)

Arguments

n

A vector of treated patients for each arm (The first element is for control)
y

A vector of treated patient outcomes for each arm (The first element is for control)
pi.star

The prior response probability. The default is 0.5
pess

The effective sample size of beta prior. The default is 2

Value

A vector posterior probability of each active treatment arm better than control

Author(s)

Ziyan Wang

Examples

n <- c(20,20,20,20)
y <- c(12,12,12,6)
ibetabinomial.post(n, y, pi.star = 0.5, pess = 2)
#[1] 0.5000000 0.5000000 0.0308018

Initializetrialparameter

Description

This function initialises the inner parameter used in simulate.trial function

Usage

Initializetrialparameter(response.probs, ns)

Arguments

response.probs

A vector of response probability of each arm
ns

A vector of accumulated number of patient at each stage

Value

A list of initialised parameters including the number of arms for this trial 'K', the number of arm active 'armleft', the index of treatment arm 'treatmentindex', the vector of total number of patients allocated to each arm 'n' the number of total number of patients survived for each arm 'y1', the matrix for true response probability of each arm at each stage 'groupwise.response.probs' which is required for the time trend study, the vector of randomisation probability for each arm 'randomprob', the array of arm assignment for each patient 'z', the array of outcome for each patient 'y', the array of the stage index for each patient 'group_indicator', the matrix of the probability of each arm to be the best at each stage 'post.prob.best.mat'.

Author(s)

Ziyan Wang

Examples

Initializetrialparameter(response.probs = c(0.4,0.6), ns = c(15,30,45,60,75,90))

#$K
#[1] 2

#$armleft
#[1] 2

#$treatmentindex
#[1] 1

#$n
#[1] 0 0

#$y1
#[1] 0 0

#$groupwise.response.probs
#     [,1] [,2]
#[1,]  0.4  0.6
#[2,]  0.4  0.6
#[3,]  0.4  0.6
#[4,]  0.4  0.6
#[5,]  0.4  0.6
#[6,]  0.4  0.6

#$randomprob
#     1   2
#[1,] 0.5 0.5

#$z
#numeric(0)

#$y
#numeric(0)

#$group_indicator
#numeric(0)

#$post.prob.best.mat
#     0 1
#[1,] 0 0
#[2,] 0 0
#[3,] 0 0
#[4,] 0 0
#[5,] 0 0
#[6,] 0 0

intbias

Description

This function estimates the mean bias of treatment - stage interaction effect

Usage

intbias(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

A matrix of mean treatment - stage interaction effect bias

Examples

## Not run: intbias(res)

Meanfunc

Description

This function reads in the output matrix of a number of trial replicates to calculate mean treatment effect estimate.

Usage

Meanfunc(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

Mean treatment effect estimates of each treatment arm

Examples

## Not run: Meanfunc(res)

modelinf.fun

Description

This function summarize the input parameters describing the model for analysis and transfer them into a list

Usage

modelinf.fun(
  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")
)

Arguments

model

The statistical model. ibb: betabinomial model / tlr: logistic model
ibb.inf

The list of information for betabinomial model including: betabinomialmodel: The betabinomial model, pi.star: prior response rate, pess: prior effective sample size
tlr.inf

The list of information for logistic model including: The mean (mu), variance (sigma), degree of freedom (df) of the intercept and the main effect of the linear terms in logistic model. reg.inf: The type of linear function in logistic model. variable.inf: Fixeffect/Mixeffect. Indicating whether a mix effect model is used (for time trend effect modelling)

Value

A list of model information including model, ibb.inf and tlr.inf

Author(s)

Ziyan Wang

Examples

modelinf.fun(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"
                 ))

Nfunc

Description

This function reads in the output matrix of a number of trial replicates to calculate mean estimate of total number of patients allocated to each arm

This function reads in the output matrix of a number of trial replicates to calculate mean estimate of total number of patients allocated to each arm

Usage

Nfunc(res)

Nfunc(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

The mean estimate of total number of patients allocated to each arm

The mean estimate of total number of patients allocated to each arm

Examples

## Not run: Nfunc(res)
## Not run: Nfunc(res)

Operation characteristic table for alternative scenario

Description

Operation characteristic table for alternative scenario using main + continuousstage model. The time trend pattern is step. The strength of time trend is 0.1 equally for all arm. The effect of time trend on true response probability is multiplicative.

Usage

OPC_alt

Format

A data frame with 3 rows and 16 variables:

Type.I.Error.or.Power

Power

Bias.1

Treatment effect bias for treatment 1

Bias.2

Treatment effect bias for treatment 2

Bias.3

Treatment effect bias for treatment 3

rMSE.1

Rooted mean squared error for treatment 1

rMSE.2

Rooted mean squared error for treatment 2

rMSE.3

Rooted mean squared error for treatment 3

N.per.arm.1

Mean total number of patient allocated to control

N.per.arm.2

Mean total number of patient allocated to treatment 1

N.per.arm.3

Mean total number of patient allocated to treatment 2

N.per.arm.4

Mean total number of patient allocated to treatment 3

Survive.per.arm.1

Mean total number of patient allocated to control

Survive.per.arm.2

Mean total number of patient survived when using treatment 1

Survive.per.arm.3

Mean total number of patient survived when usin treatment 2

Survive.per.arm.4

Mean total number of patient survived when usin treatment 3

N

Mean total number of patient in a trial

Operation characteristic table for null scenario

Description

Operation characteristic table for null scenario using main and main + continuousstage model. The main effect model was run for a null scenario with and without time trend. The time trend pattern is step. The strength of time trend is 0.1 equally for all arm. The effect of time trend on true response probability is multiplicative.

Usage

OPC_null

Format

A data frame with 3 rows and 16 variables:

Type.I.Error.or.Power

Family wise error rate

Bias.1

Treatment effect bias for treatment 1

Bias.2

Treatment effect bias for treatment 2

Bias.3

Treatment effect bias for treatment 3

rMSE.1

Rooted mean squared error for treatment 1

rMSE.2

Rooted mean squared error for treatment 2

rMSE.3

Rooted mean squared error for treatment 3

N.per.arm.1

Mean total number of patient allocated to control

N.per.arm.2

Mean total number of patient allocated to treatment 1

N.per.arm.3

Mean total number of patient allocated to treatment 2

N.per.arm.4

Mean total number of patient allocated to treatment 3

Survive.per.arm.1

Mean total number of patient allocated to control

Survive.per.arm.2

Mean total number of patient survived when using treatment 1

Survive.per.arm.3

Mean total number of patient survived when usin treatment 2

Survive.per.arm.4

Mean total number of patient survived when usin treatment 3

N

Mean total number of patient in a trial

Operation characteristic table for Trial.simulation() null scenario

Description

Operation characteristic table for null scenario using main model.

Usage

OPC_Trial.simulation

Format

A data frame with 1 rows and 8 variables:

Type.I.Error.or.Power

Power

Bias

Treatment effect bias for treatment 1

rMSE

Rooted mean squared error for treatment effect 1

N.per.arm.1

Mean total number of patient allocated to control

N.per.arm.2

Mean total number of patient allocated to treatment 1

Survive.per.arm.1

Mean total number of patient allocated to control

Survive.per.arm.2

Mean total number of patient survived when using treatment 1

N

Mean total number of patient in a trial

A list of data from Gaussian process and trial simulation for asymmetric cutoff screening.

Description

A list of data from Gaussian process and trial simulation for asymmetric cutoff screening.

Usage

optimdata_asy

Format

A list with four element:

next.cutoff

The cutoff value for the next evaluation

prediction

A list of values from Gaussian process model

ESS

A two column twenty five rows matrix with the effective sample size for each cutoff pair under both null (first column) and alternative (second column) scenario

testeddata

A data frame containing each tested cutoff pair (column two and three for efficacy and futility, respectively), their FWER under null (column one) and conjunctive power under alternative (column four)

A list of data from Gaussian process for symmetric cutoff screening.

Description

A list of data from Gaussian process for symmetric cutoff screening.

Usage

optimdata_sym

Format

A list with two element:

next.cutoff

The cutoff value for the next evaluation

prediction

A list of values from Gaussian process model

tpIE

A vector of type I error rate data

cutoff

A vector of cutoff data

OutputStats.initialising

Description

This function initializes the output matrix including all evaluation metrics based on input information

Usage

OutputStats.initialising(variable.inf, reg.inf, ns, K)

Arguments

variable.inf

The parameter information in the model
reg.inf

The model information. For the fixed effect model, the input of reg.inf can be main, main + stage_continuous, main * stage_continuous, main + stage_discrete, main * stage_discrete. For the mixed effect model, the reg.inf is invalid.
ns

A vector of accumulated number of patient at each stage
K

Total number of arm including control

Value

The empty output matrix including different evaluation metrics.

Author(s)

Ziyan Wang

Examples

OutputStats.initialising(
    variable.inf = "Fixeffect",
    reg.inf = "main",
    ns = c(15, 30, 45, 60, 75),
    K = 2)

perHtypeIerror_powerfunc

Description

This function reads in the output matrix of a number of trial replicates to calculate the error rate

Usage

perHtypeIerror_marginalpowerfunc(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

"per-hypothesis" Type I error rate or power (marginal power) for each treatment - control comparison

Author(s)

Ziyan Wang

Examples

## Not run: perHtypeIerror_powerfunc(res)

Cutoff screening example: the predicted value from quadratic model

Description

The predicted value from quadratic model for famliy wise error rate vs cutoff value plotting

Usage

predictedtpIEinformd

Format

A data frame with 1001 rows and 1 variables:

predictedtpIEinformd

The predicted FWER value of a large grid

Randomisation.inf

Description

This function checks the validity of the randomisation information input

Usage

Randomisation.inf(
  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))
)

Arguments

Random.inf

A list of adaptive randomisation information. 'Fixratio' a indicator of whether the randomisation process uses fix ratio. Default is FALSE. 'Fixratiocontrol' the numerical value indicating the randomisation weight of the control arm compared to the treatment arms. Default is NA for Fixratio = FALSE. 'BARmethod' the Bayesian adaptive randomisation type. Default is "Thall" indicating the use of Thall's approach in the randomisation process. The other value is 'Trippa'. 'Thall.tuning.inf' the list of tuning parameter for Thall's approach including 'tuningparameter' (Default is "Fixed" indicating that the tuning paramter is fixed for all stages) and 'fixvalue' (Default is 1).

Value

A list of input randomisation information

Author(s)

Ziyan Wang

Examples

Randomisation.inf(Random.inf = list(
Fixratio = FALSE,
Fixratiocontrol = NA,
BARmethod = "Thall",
Thall.tuning.inf = list(tuningparameter = "Fixed",  fixvalue = 1),
Trippa.tuning.inf = list(a = NA, b = NA)
))

Cutoff screening example: the recommended grid value at each time point

Description

he recommended grid value at each time point. There are 20 cutoff value explored

Usage

recommandloginformd

Format

A data frame with 20 rows and 1 variables:

recommandloginformd

The cutoff value at each time point

resultrtostats

Description

This is an inner function of the function resultstantoRfunc

Usage

resultrtostats(
  trteff = NA,
  treatmentindex = NA,
  armleft,
  K,
  group,
  reg.inf,
  fit,
  ns
)

Arguments

trteff

Stan posterior samples of treatment effect sample distribution
treatmentindex

A vector of treatment index at the beginning of a trial
armleft

The number of treatment left in the platform (>2)
K

Total number of arms at the beginning
group

The current stage
reg.inf

The information of how much accumulated information will be used
fit

The stan output
ns

A vector of accumulated number of patient at each stage

Value

A list of stan result inference stats1: A vector of posterior probability for all treatment arms including dropped and active treatment arm stats4: The mean treatment effect estimate of each treatment compared to control stats5: The variance of treatment effect estimate of each treatment compared to control post.prob.btcontrol: a vector including Posterior probability of each active treatment arm better than control

Author(s)

Ziyan Wang

Examples

## Not run: resultrtostats(trteff = NA, treatmentindex = NA, armleft, K, group, reg.inf, fit, ns)

resultrtostats.rand

Description

The inner function of function resultstantoRfunc.rand

Usage

resultrtostats.rand(
  trteff = NA,
  treatmentindex = NA,
  armleft,
  K,
  group,
  fit,
  ns
)

Arguments

trteff

Stan posterior samples of treatment effect sample distribution
treatmentindex

A vector of treatment index at the beginning of a trial
armleft

The number of treatment left in the platform (>2)
K

Total number of arms at the beginning
group

The current stage
fit

The stan output
ns

A vector of accumulated number of patient at each stage

Value

A list of stan result inference stats1: A vector of posterior probability for all treatment arms including dropped and active treatment arm stats4: The mean treatment effect estimate of each treatment compared to control stats5: The variance of treatment effect estimate of each treatment compared to control post.prob.btcontrol: a vector including Posterior probability of each active treatment arm better than control

Author(s)

Ziyan Wang

Examples

## Not run: resultrtostats.rand(trteff = NA, treatmentindex = NA, armleft, K, group, fit, ns)

resultstantoRfunc

Description

This function summarise the fix effect stan output data to and transform them to be readable.

Usage

resultstantoRfunc(
  group,
  reg.inf,
  variable.inf,
  fit,
  armleft,
  treatmentindex,
  K,
  ns
)

Arguments

group

The current stage
reg.inf

The information of how much accumulated information will be used
variable.inf

The information of whether to use random effect model or fix effect model.
fit

The stan output
armleft

The number of treatment left in the platform (>2)
treatmentindex

A vector of treatment index at the beginning of a trial
K

Total number of arms at the beginning
ns

A vector of accumulated number of patient at each stage

Value

A list of stan result inference stats1: A vector of posterior probability for all treatment arms including dropped and active treatment arm stats4: The mean treatment effect estimate of each treatment compared to control stats5: The variance of treatment effect estimate of each treatment compared to control post.prob.btcontrol: a vector including Posterior probability of each active treatment arm better than control stats6: A vector of mean stage (time trend) effect estimate stats7: A vector of mean treatment - stage (time trend) interaction effect estimate sampefftotal: The posterior samples of response probability of each active arm on logit scale. This can be transformed to probit scale by using inv.logit() function.

Author(s)

Ziyan Wang

Examples

## Not run: resultstantoRfunc(group, reg.inf, fit, armleft, treatmentindex, K, ns)

resultstantoRfunc.rand

Description

This function summarise the mix effect stan output data to and transform them to be readable.

Usage

resultstantoRfunc.rand(group, fit, armleft, treatmentindex, K, ns)

Arguments

group

The current stage
fit

The stan output
armleft

The number of treatment left in the platform (>2)
treatmentindex

A vector of treatment index at the beginning of a trial
K

Total number of arms at the beginning
ns

A vector of accumulated number of patient at each stage

Value

A list of stan result inference stats1: A vector of posterior probability for all treatment arms including dropped and active treatment arm stats4: The mean treatment effect estimate of each treatment compared to control stats5: The variance of treatment effect estimate of each treatment compared to control post.prob.btcontrol: a vector including Posterior probability of each active treatment arm better than control stats6: A vector of mean stage (time trend) effect estimate stats7: A vector of mean treatment - stage (time trend) interaction effect estimate sampefftotal: The posterior samples of response probability of each active arm on logit scale. This can be transformed to probit scale by using inv.logit() function.

Author(s)

Ziyan Wang

Examples

## Not run: resultstantoRfunc.rand(group, fit, armleft, treatmentindex, K, ns)

Save.resulttoRDatafile

Description

This function generates the name of file for output table and dataset

Usage

Save.resulttoRDatafile(
  input.info = list(response.probs = c(0.4, 0.4), ns = c(30, 60, 90, 120, 150), max.ar =
    0.75, rand.type = "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)), trend.inf = list(trend.type = "step", trend.effect = c(0,
    0), trend_add_or_multip = "mult"))
)

Arguments

input.info

A list of input information require for trial simulation

Value

A list of name for table and dataset

Author(s)

Ziyan Wang

Examples

Save.resulttoRDatafile(
input.info = list(
  response.probs = c(0.4, 0.4),
  ns = c(30, 60, 90, 120, 150),
  max.ar = 0.75,
  rand.type = "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"
    )
  ),
  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"
  )
))

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"
               ))

Sperarmfunc

Description

This function reads in the output matrix of a number of trial replicates to calculate mean total number of survived patients of each arm

This function reads in the output matrix of a number of trial replicates to calculate mean total number of survived patients of each arm

Usage

Sperarmfunc(res)

Sperarmfunc(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

The mean total number of survived patients of each arm

The mean total number of survived patients of each arm

Examples

## Not run: Sperarmfunc(res)
## Not run: Sperarmfunc(res)

stan.logisticmodeltrans

Description

This function transform the data in trial simulation to the data required for stan modelling

Usage

stan.logisticmodeltrans(
  z,
  y,
  randomprob,
  group_indicator,
  armleft,
  group,
  variable.inf,
  reg.inf
)

Arguments

z

A vector of all treatment index data from the beginning of a trial
y

A vector of all outcome data from the beginning of a trial
randomprob

A named vector of randomisation probability to each arm
group_indicator

A vector for the stage at which each patient was treated
armleft

The number of treatment left in the platform (>2)
group

The current stage
variable.inf

Fixeffect/Mixeffect for logistic model parameter
reg.inf

The information of how much accumulated information will be used

Value

A list of information require for the stan model including: zdropped: The vector of treatment index for each patient whose treatment arm is active at current stage. ydropped: The vector of outcome index for each patient whose treatment arm is active at current stage. Ndropped: The total number of patients that are treated with active treatment arms at current stage. group_indicator_dropped: The vector of stage index for each patient whose treatment arm is active at current stage. zlevel: The active treatment arm index at current stage xdummy: A design matrix transformed from zdropped and group_indicator_dropped for modelling

Author(s)

Ziyan Wang

Examples

stan.logisticmodeltrans(
z = c(1,2,1,2,2,1,2,1),
y = c(0,0,0,0,1,1,1,1),
randomprob = matrix(c( 0.5, 0.5), ncol = 2, dimnames = list(c("Stage1"), c("1", "2"))),
group_indicator = c(1,1,1,1,1,1,1,1),
armleft = 2,
group = 1,
variable.inf = "Fixeffect",
reg.inf = "main")

Stopboundinf

Description

This function summaries and checks stopping boundary information.

Usage

Stopboundinf(
  Stop.type = "Early-Pocock",
  Boundary.type = "Symmetric",
  cutoff = c(0.9928, 0.0072)
)

Arguments

Stop.type

The type of stopping boundary should be "Early-Pocock", "Early-OBF" and "Noearly". Default is "Early-Pocock" which is the Pocock boundary with early stopping.
Boundary.type

Whether the futility boundary and the efficacy boundary are the same conservative. Default is "Symmetric" which means they are as conservative as each other. Boundary.type = "Asymmetric" means that the efficacy boundary and the futility boundary are not as conservative as each other
cutoff

= c(cutoff1, cutoff2). A numerical vector of cutoff value for each boundary. The first element is the efficacy boundary cutoff. The second element is the futility boundary cutoff Pr(theta_1 > theta_0|D_n) > cutoff1. Should input the cutoff1 for efficacy boundary as the first element Pr(theta_1 < theta_0|D_n) < cutoff2. Should input the cutoff2 for futility boundary as the first element

Value

The list of information required for boundary construction function 'Stopbound.inf'

Author(s)

Ziyan Wang

Examples

Stop.type = "Early-Pocock" #(Pocock boundarty is a flat boundary across time)
Boundary.type = "Symmetric"
cutoff = c(0.9928, 0.0072)

Stopbound.inf = Stopboundinf(Stop.type, Boundary.type, cutoff)
#Stopbound.inf
#$Stop.type
# [1] "Early-Pocock"
#$Boundary.type
#[1] "Symmetric"
#$cutoff
# [1] 0.9928 0.0072

Title

Description

Title

Usage

testing_and_armdropping(
  K,
  armleft,
  post.prob.btcontrol,
  group,
  cutoffeff,
  cutoffful,
  treatmentindex,
  test.type
)

Arguments

K

A numeric value indicating the total number of arm at the beginning of trial including both control and treatment.
armleft

A numeric vector indicating the number of active arms before this interim analysis;
post.prob.btcontrol

A numeric vector of posterior probability of each treatment arm better than control
group

A numeric value. The current stage index.
cutoffeff

A numeric vector of the cutoff value at each stage for efficacy boundary.
cutoffful

A numeric vector of the cutoff value at each stage for futility boundary.
treatmentindex

A numeric vector of the current active treatment arm index
test.type

A character indicating which hypothesis testing we are use. "Oneside": H_0: \pi_k \leq \pi_0; H_0: \pi_k \> \pi_0 "Twoside": H_0: \pi_k \eq \pi_0; H_0: \pi_k \neq \pi_0

Value

A list of information including armleft: the number of active arms after this interim analysis; treatmentindex: the index vector of active arm after this interim analysis; stats3: the vector of conclusion on whether null hypothesis is rejected

Examples

testing_and_armdropping(
K = 4,
armleft = 4,
post.prob.btcontrol = c(0.5,0.99,0.02),
group = 3,
cutoffeff = c(1, 0.99, 0.975, 0.96, 0.95),
cutoffful = c(0, 0.01, 0.025, 0.04, 0.05),
treatmentindex = c(1,2,3),
test.type = "Oneside")

testing_and_armdropping(
K = 4,
armleft = 4,
post.prob.btcontrol = c(0.5,0.99,0.02),
group = 3,
cutoffeff = c(1, 0.99, 0.975, 0.96, 0.95),
cutoffful = c(0, 0.01, 0.025, 0.04, 0.05),
treatmentindex = c(1,2,3),
test.type = "Twoside")

Timetrend.fun

Description

This function generate the time trend function based on trend information. This function also check the validity of the input time trend information.

Usage

Timetrend.fun(trend.inf)

Arguments

trend.inf

The list of information for time trend effect including 'trend.type', 'trend.effect', 'trend_add_or_multip'. 'trend.type' is the shape of time trend. Default is "step". Other types are "linear", "inverse.U.linear", "plateau". "trend.effect" the vector of the strength of time trend for each arm. The first element is for the control arm. "trend_add_or_multip" the pattern of time trend affecting the true response probability. Default is "mult".

Value

A list containing the time trend function according to input trend.type variable, and a indicator of whether there is a time trend in data generation based on input trend information

Author(s)

Ziyan Wang

Examples

Timetrend.fun(trend.inf = list(
               trend.type = "step",
               trend.effect = c(0, 0),
               trend_add_or_multip = "mult"
               ))

Trial simulation

Description

This function simulates and does final analysis of a trial with one scenario. The time cost of this function depend on the cpu cores of the user's cpmputer.

Usage

Trial.simulation(
  ntrials = 5000,
  trial.fun = simulatetrial,
  input.info = list(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")),
  cl = 2
)

Arguments

ntrials

A numeric variable indicating how many trial replicates you want to run. Default is 5000.
trial.fun

The function of trial simulation for more see simulatetrial
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 list of output including the final output of each trial replicates called 'result' The analysis result table of the specific trial called 'OPC' and the file name for saving these output on the computer

Author(s)

Ziyan Wang

Examples

set.seed(1)
Trial.simulation(ntrials = 2, cl = 2)

trtbias

Description

This function estimates the mean bias of treatment effect

Usage

trtbias(res, trueeffect)

Arguments

res

A list of output matrix of a number of trial replicates
trueeffect

A vector of true treatment effect in each scenario

Value

A matrix of mean treatment effect bias

Examples

## Not run: trtbias(res, trueeffect)

trteffect

Description

This function estimates the mean treatment effect bias and its rooted mean squared error

Usage

trteffect(res, trueeff)

Arguments

res

A list of output matrix of a number of trial replicates
trueeff

A vector of true treatment effect in each scenario

Value

A vector of mean treatment effect bias and its rooted mean squared error

Examples

## Not run: trteffect(res, trueeff)

varfunc

Description

This function reads in the output matrix of a number of trial replicates to calculate the variance of treatment effect estimate.

Usage

varfunc(res)

Arguments

res

A list of output matrix of a number of trial replicates

Value

The variance of Treatment effect estimates of each treatment arm

Examples

## Not run: varfunc(res)