Simulate and Analyse Bayesian Platform Trial with Time Trend
AdaptiveRandomisation
alphaspending
ARmethod
The 'BayesianPlatformDesignTimeTrend' package.
Boundaryconstruction
conjuncativepower_or_FWER
A demo for cutoff screening using Bayesian optimisation
demo_Cutoffscreening
demo_multscenario
disconjunctivepowerfunc
GP.optim: optimiser to give the next cutoff for evaluation
ibetabinomial.post
Initializetrialparameter
intbias
Meanfunc
modelinf.fun
Nfunc
OutputStats.initialising
perHtypeIerror_powerfunc
Randomisation.inf
resultrtostats.rand
resultrtostats
resultstantoRfunc.rand
resultstantoRfunc
Save.resulttoRDatafile
simulatetrial
Sperarmfunc
stan.logisticmodeltrans
Stopboundinf
testing_and_armdropping
Timetrend.fun
Trial simulation
trtbias
trteffect
varfunc
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>.
Useful links