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:
BayesianPlatformDesignTimeTrend_1.2.0.tar.gz
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BayesianPlatformDesignTimeTrend.pdf |BayesianPlatformDesignTimeTrend.html✨
BayesianPlatformDesignTimeTrend/json (API)
NEWS
# 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
- OPC_Trial.simulation - Operation characteristic table for Trial.simulation() null scenario
- OPC_alt - Operation characteristic table for alternative scenario
- OPC_null - Operation characteristic table for null scenario
- dataloginformd - Cutoff screening example: the details of grid
- 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
analysisbayesian-adaptive-randomisationclinial-trialgroup-sequential-designsmultiarm-multistage-trialsplatform-trialssimulation
Last updated 1 years agofrom:5a86705bb6. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-win-x86_64 | NOTE | Oct 26 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 26 2024 |
R-4.4-win-x86_64 | NOTE | Oct 26 2024 |
R-4.4-mac-x86_64 | NOTE | Oct 26 2024 |
R-4.4-mac-aarch64 | NOTE | Oct 26 2024 |
R-4.3-win-x86_64 | NOTE | Oct 26 2024 |
R-4.3-mac-x86_64 | NOTE | Oct 26 2024 |
R-4.3-mac-aarch64 | NOTE | Oct 26 2024 |
Exports:AdaptiveRandomisationalphaspendingARmethodBoundaryconstructionconjuncativepower_or_FWERdemo_Cutoffscreeningdemo_Cutoffscreening.GPdemo_multscenariodisconjunctivepowerfuncGP.optimibetabinomial.postInitializetrialparameterintbiasMeanfuncmodelinf.funNfuncOutputStats.initialisingperHtypeIerror_marginalpowerfuncRandomisation.infresultrtostatsresultrtostats.randresultstantoRfuncresultstantoRfunc.randSave.resulttoRDatafilesimulatetrialSperarmfuncstan.logisticmodeltransStopboundinftesting_and_armdroppingTimetrend.funTrial.simulationtrtbiastrteffectvarfunc
Dependencies:abindbackportsBHBiocManagerbootbroomcallrcarcarDatacheckmatecliclustercodetoolscolorspacecorrplotcowplotcpp11DerivdescdirectlabelsdistributionaldoBydoParalleldplyrfansifarverforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableinlineisobanditeratorslabelinglaGPlatticelhslifecyclelme4loomagrittrmaptreeMASSMatrixMatrixModelsmatrixStatsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgbuildpkgconfigplyrpolynomposteriorprocessxpspurrrquadprogquantregQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshapereshape2rlangrpartrstanrstantoolsrstatixscalesSparseMStanHeadersstringistringrsurvivaltensorAtgptibbletidyrtidyselectutf8vctrsviridisLitewithr
MAMS-CutoffScreening-GP-Asymmetric-tutorial
Rendered fromMAMS-CutoffScreening-GP-Asymmetric-tutorial.Rmd
usingknitr::rmarkdown
on Oct 26 2024.Last update: 2023-10-04
Started: 2023-10-04
MAMS-CutoffScreening-GP-Symmetric-tutorial
Rendered fromMAMS-CutoffScreening-GP-Symmetric-tutorial.Rmd
usingknitr::rmarkdown
on Oct 26 2024.Last update: 2023-10-04
Started: 2023-10-04
MAMS-CutoffScreening-tutorial
Rendered fromMAMS-CutoffScreening-tutorial.Rmd
usingknitr::rmarkdown
on Oct 26 2024.Last update: 2023-07-10
Started: 2023-03-07
MAMS-trial-simulation-tutorial
Rendered fromMAMS-trial-simulation-tutorial.Rmd
usingknitr::rmarkdown
on Oct 26 2024.Last update: 2023-07-10
Started: 2023-03-07