Package: BayesianPlatformDesignTimeTrend 1.2.0

BayesianPlatformDesignTimeTrend: Simulate and Analyse Bayesian Platform Trial with Time Trend

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]

BayesianPlatformDesignTimeTrend_1.2.0.tar.gz
BayesianPlatformDesignTimeTrend_1.2.0.zip(r-4.7-x86_64)BayesianPlatformDesignTimeTrend_1.2.0.zip(r-4.7-arm64)BayesianPlatformDesignTimeTrend_1.2.0.zip(r-4.6-x86_64)BayesianPlatformDesignTimeTrend_1.2.0.zip(r-4.6-arm64)BayesianPlatformDesignTimeTrend_1.2.0.zip(r-4.5-x86_64)
BayesianPlatformDesignTimeTrend_1.2.0.tgz(r-4.6-x86_64)BayesianPlatformDesignTimeTrend_1.2.0.tgz(r-4.6-arm64)BayesianPlatformDesignTimeTrend_1.2.0.tgz(r-4.5-x86_64)BayesianPlatformDesignTimeTrend_1.2.0.tgz(r-4.5-arm64)
BayesianPlatformDesignTimeTrend_1.2.0.tar.gz(r-4.7-arm64)BayesianPlatformDesignTimeTrend_1.2.0.tar.gz(r-4.7-x86_64)BayesianPlatformDesignTimeTrend_1.2.0.tar.gz(r-4.6-arm64)BayesianPlatformDesignTimeTrend_1.2.0.tar.gz(r-4.6-x86_64)
BayesianPlatformDesignTimeTrend_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BayesianPlatformDesignTimeTrend/json (API)

# Install 'BayesianPlatformDesignTimeTrend' in R:
install.packages('BayesianPlatformDesignTimeTrend', repos = c('https://zxw834.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zxw834/bayesianplatformdesigntimetrend/issues

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3
Datasets:
  • dataloginformd - Cutoff screening example: the details of grid
  • OPC_alt - Operation characteristic table for alternative scenario
  • OPC_null - Operation characteristic table for null scenario
  • OPC_Trial.simulation - Operation characteristic table for Trial.simulation() null scenario
  • optimdata_asy - A list of data from Gaussian process and trial simulation for asymmetric cutoff screening.
  • optimdata_sym - A list of data from Gaussian process for symmetric cutoff screening.
  • predictedtpIEinformd - Cutoff screening example: the predicted value from quadratic model
  • recommandloginformd - Cutoff screening example: the recommended grid value at each time point

On CRAN:

Conda:

analysisbayesian-adaptive-randomisationclinial-trialgroup-sequential-designsmultiarm-multistage-trialsplatform-trialssimulationonetbbcpp

4.51 score 16 scripts 480 downloads 34 exports 111 dependencies

Last updated from:5a86705bb6. Checks:13 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE306
linux-devel-x86_64NOTE301
source / vignettesOK513
linux-release-arm64NOTE287
linux-release-x86_64NOTE339
macos-release-arm64NOTE316
macos-release-x86_64NOTE506
macos-oldrel-arm64NOTE297
macos-oldrel-x86_64NOTE695
windows-devel-arm64NOTE533
windows-devel-x86_64NOTE533
windows-release-arm64NOTE530
windows-release-x86_64NOTE388
windows-oldrel-x86_64NOTE379
wasm-releaseOK187

Exports:AdaptiveRandomisationalphaspendingARmethodBoundaryconstructionconjuncativepower_or_FWERdemo_Cutoffscreeningdemo_Cutoffscreening.GPdemo_multscenariodisconjunctivepowerfuncGP.optimibetabinomial.postInitializetrialparameterintbiasMeanfuncmodelinf.funNfuncOutputStats.initialisingperHtypeIerror_marginalpowerfuncRandomisation.infresultrtostatsresultrtostats.randresultstantoRfuncresultstantoRfunc.randSave.resulttoRDatafilesimulatetrialSperarmfuncstan.logisticmodeltransStopboundinftesting_and_armdroppingTimetrend.funTrial.simulationtrtbiastrteffectvarfunc

Dependencies:abindbackportsBHBiocManagerbootbroomcallrcarcarDatacheckmatecliclustercodetoolscolorspacecorrplotcowplotcpp11data.tableDerivdescdirectlabelsdistributionaldoBydoParalleldplyrfarverforeachforecastFormulafracdiffgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableinlineisobanditeratorslabelinglaGPlatticelhslifecyclelme4lmtestloomagrittrmaptreeMASSMatrixMatrixModelsmatrixStatsmgcvminqamodelrnlmenloptrnnetnumDerivotelpbkrtestpillarpkgbuildpkgconfigplyrpolynomposteriorprocessxpspurrrquantregQuickJSRR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasreshapereshape2rlangrpartrstanrstantoolsrstatixS7scalesSparseMStanHeadersstringistringrsurvivaltensorAtgptibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

MAMS-CutoffScreening-GP-Asymmetric-tutorial
Four arm trial cutoff screening

Last update: 2023-10-04
Started: 2023-10-04

MAMS-CutoffScreening-GP-Symmetric-tutorial
Four arm trial cutoff screening

Last update: 2023-10-04
Started: 2023-10-04

MAMS-CutoffScreening-tutorial
Four arm trial cutoff screening

Last update: 2023-07-10
Started: 2023-03-07

MAMS-trial-simulation-tutorial
Four arm trial simulation

Last update: 2023-07-10
Started: 2023-03-07

Readme and manuals

Help Manual

Help pageTopics
The 'BayesianPlatformDesignTimeTrend' package.BayesianPlatformDesignTimeTrend-package BayesianPlatformDesignTimeTrend
AdaptiveRandomisationAdaptiveRandomisation
alphaspendingalphaspending
ARmethodARmethod
BoundaryconstructionBoundaryconstruction
conjuncativepower_or_FWERconjuncativepower_or_FWER
Cutoff screening example: the details of griddataloginformd
demo_Cutoffscreeningdemo_Cutoffscreening
A demo for cutoff screening using Bayesian optimisationdemo_Cutoffscreening.GP
demo_multscenariodemo_multscenario
disconjunctivepowerfuncdisconjunctivepowerfunc
GP.optim: optimiser to give the next cutoff for evaluationGP.optim
ibetabinomial.postibetabinomial.post
InitializetrialparameterInitializetrialparameter
intbiasintbias
MeanfuncMeanfunc
modelinf.funmodelinf.fun
NfuncNfunc
Operation characteristic table for alternative scenarioOPC_alt
Operation characteristic table for null scenarioOPC_null
Operation characteristic table for Trial.simulation() null scenarioOPC_Trial.simulation
A list of data from Gaussian process and trial simulation for asymmetric cutoff screening.optimdata_asy
A list of data from Gaussian process for symmetric cutoff screening.optimdata_sym
OutputStats.initialisingOutputStats.initialising
perHtypeIerror_powerfuncperHtypeIerror_marginalpowerfunc
Cutoff screening example: the predicted value from quadratic modelpredictedtpIEinformd
Randomisation.infRandomisation.inf
Cutoff screening example: the recommended grid value at each time pointrecommandloginformd
resultrtostatsresultrtostats
resultrtostats.randresultrtostats.rand
resultstantoRfuncresultstantoRfunc
resultstantoRfunc.randresultstantoRfunc.rand
Save.resulttoRDatafileSave.resulttoRDatafile
simulatetrialsimulatetrial
SperarmfuncSperarmfunc
stan.logisticmodeltransstan.logisticmodeltrans
StopboundinfStopboundinf
Titletesting_and_armdropping
Timetrend.funTimetrend.fun
Trial simulationTrial.simulation
trtbiastrtbias
trteffecttrteffect
varfuncvarfunc