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BayesianPlatformDesignTimeTrend  

Simulate and Analyse Bayesian Platform Trial with Time Trend
View on CRAN: Click here


Download and install BayesianPlatformDesignTimeTrend package within the R console
Install from CRAN:
install.packages("BayesianPlatformDesignTimeTrend")

Install from Github:
library("remotes")
install_github("cran/BayesianPlatformDesignTimeTrend")

Install by package version:
library("remotes")
install_version("BayesianPlatformDesignTimeTrend", "1.2.3")



Attach the package and use:
library("BayesianPlatformDesignTimeTrend")
Maintained by
Ziyan Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-03-15
Latest Update: 2023-12-07
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) and Wathen et al. (2017) . The randomisation algorithm is described in: Zhao W . The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) and Bofill Roig et al. (2022) .
How to cite:
Ziyan Wang (2023). BayesianPlatformDesignTimeTrend: Simulate and Analyse Bayesian Platform Trial with Time Trend. R package version 1.2.3, https://cran.r-project.org/web/packages/BayesianPlatformDesignTimeTrend. Accessed 07 Jun. 2026.
Previous versions and publish date:
1.0.0 (2023-03-15 19:40), 1.0.1 (2023-05-02 10:10), 1.1.0 (2023-05-05 09:45), 1.1.1 (2023-06-12 15:00), 1.1.2 (2023-06-25 07:10), 1.1.3 (2023-08-22 09:20), 1.2.0 (2023-10-05 15:30), 1.2.1 (2023-10-11 15:30), 1.2.2 (2023-11-22 02:00)
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Complete documentation for BayesianPlatformDesignTimeTrend
Functions, R codes and Examples using the BayesianPlatformDesignTimeTrend R package
Some associated functions: ARmethod . AdaptiveRandomisation . BayesianPlatformDesignTimeTrend-package . Boundaryconstruction . GP.optim . Initializetrialparameter . Meanfunc . Nfunc . OPC_Trial.simulation . OPC_alt . OPC_null . OutputStats.initialising . Randomisation.inf . Save.resulttoRDatafile . Sperarmfunc . Stopboundinf . Timetrend.fun . Trial.simulation . alphaspending . conjuncativepower_or_FWER . dataloginformd . demo_Cutoffscreening.GP . demo_Cutoffscreening . demo_multscenario . disconjunctivepowerfunc . ibetabinomial.post . intbias . modelinf.fun . optimdata_asy . optimdata_sym . perHtypeIerror_marginalpowerfunc . predictedtpIEinformd . recommandloginformd . resultrtostats . resultrtostats.rand . resultstantoRfunc . resultstantoRfunc.rand . simulatetrial . stan.logisticmodeltrans . testing_and_armdropping . trtbias . trteffect . varfunc . 
Some associated R codes: Analysis_listofanalysisfunction.R . BayesianPlatformDesignTimeTrend-package.R . Demo_CutoffScreening.R . Demo_CutoffScreening_GP.R . Demo_multiplescenariotrialsimulation.R . MainFunction.R . Plot_listoffunctionforplotgeneration.R . Saveoutput_toRData.R . Screening_GP_optim.R . Simulation_AdaptiveRandomisationmethodRatioCalc.R . Simulation_Boundaryconstruction.R . Simulation_Hypothesistesting_and_armdropping.R . Simulation_Outputmatrixinitialization.R . Simulation_Parameterinitialisation.R . Simulation_Randomisationinformationcheck.R . Simulation_Randomisepatientstotrt.R . Simulation_Stanlogisticdatatransform.R . Simulation_Stansamplesummary_Fixeffect.R . Simulation_Stansamplesummary_Mixeffect.R . Simulation_Stansamplesummarytolist_Fixeffect.R . Simulation_Stansamplesummarytolist_Mixeffect.R . Simulation_Timetrendfunctiongeneration.R . Transferdatatolist_Modelinf.R . Transferdatatolist_Stopboundinf.R . TrialSimulation.R . datafile_document.R . ibetabinomialpost.R . stanmodels.R .  Full BayesianPlatformDesignTimeTrend package functions and examples
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