This release fix one command in fix effect model analysis of main effect that is confusing.
resultrtostats. THe
calculation of “stats4” was the sum of main effect and interaction
effect when using the model with interaction term (main *
stage_continuous / main * stage_discrete). The sum was used to calculate
the posterior probability of superior or inferior. However, “stats4”
only represents the mean values of main effect in the final output
matrix. The wrong value was given to “stats4”. This is the same for
“stats5” which is the standard errors of the main effect in the
modelThis release fix two bugs.
GP.optim where the formula
of information weighed randomisation is wrong.demo_Cutoffscreening where
the nextcutoff vector for sample may have only one element. This will
lead to the error in function sample when you only want to
sample one value greater than 1. The argument ‘ntrials’ in each example
should be large (> 100) instead 2 to make the example more like an
actual simulation example. I use ntrials = 2 in the example to speed up
the check process.This release fix one bug reported by the cran team.
GP.optim where the
nextcutoff vector for sample may have only one element. This will lead
to the error in function sample when you only want to
sample one value greater than 1.This release implements additional method of cutoff screening using Bayesian optimization.
Add a new Demo function called
demo_Cutoffscreening_GP. The function indicates how to use
Gaussian process-based Bayesian optimisation algorithm to calibrate the
critical value for stopping boundary under the null scenario to control
the Type I error rate or FWER before trial simulation.
Add a function GP_optim which returns a cutoff value
for the next evaluation during the cutoff screening process. The
function is used in demo demo_Cutoffscreening_GP.
I fixed a bug where the fix ratio allocation method code is not suitable for the multi-arm trial
Old code: rpk[-1] = rep((armleft - 1) / (armleft - 1 + Fixratiocontrol), armleft - 1)
New code: rpk[-1] = rep(1 / (armleft - 1 + Fixratiocontrol), armleft - 1)
I added a check command for max.ar which is the upper boundary for randomisation ratio for each arm. The command is added in the MainFunction.R. Details are shown below.
#-max.ar check if (1 - max.ar > 1/K){ stop(“Error: The lower allocation ratio should be at least 1/K. Please check the number of arm at the beginning and the max.ar”) }
Another change is the randomisation ratio adjustment in the Simulation_AdaptiveRandomisationmethodRatioCalc.R.
I modified the randomisation ratio adjustment command for Thall’s approach.
##—-Original code—— (This code only protects the control arm’s allocation ratio)
rpk = matrix(rep(0, armleft), ncol = armleft)
randomprob = matrix(rep(0, K), ncol = K)
colnames(rpk) = c(1, treatmentindex + 1)
colnames(randomprob) = seq(1, K)
rpk[1] = alloc.prob.best[1]
rpk[-1] = alloc.prob.best[-1][treatmentindex]
rpk = rpk / sum(rpk)
lower = ifelse(rpk[1] < (1 - max.ar), 1 - max.ar, rpk[1])
rpk[1] = lower
upper = ifelse(rpk[1] > max.ar, max.ar, rpk[1])
rpk[1] = upper
rpk[-1] = (1 - rpk[1]) * rpk[-1] / sum(rpk[-1])
randomprob[as.numeric(colnames(rpk))] = randomprob[as.numeric(colnames(rpk))] + rpk
##—-New code—— (This code protects all arms’ allocation ratio. Also, this code work together with max.ar check code)
rpk = matrix(rep(0,armleft),ncol = armleft)
randomprob = matrix(rep(0,K),ncol = K)
colnames(rpk) = c(1,treatmentindex+1)
colnames(randomprob) = seq(1,K)
rpk[1] = alloc.prob.best[1]
rpk[-1] = alloc.prob.best[-1][treatmentindex]
rpk = rpk/sum(rpk)
lower = ifelse(rpk<(1-max.ar),1-max.ar,rpk)
rpk = lower
upper = ifelse(rpk>max.ar,max.ar,rpk)
rpk = upper
rpk[!(rpk==(1-max.ar))]=(1-sum(rpk[rpk==1-max.ar]))*(rpk[!(rpk==1-max.ar)]/sum(rpk[!(rpk==1-max.ar)]))
randomprob[as.numeric(colnames(rpk))] = randomprob[as.numeric(colnames(rpk))]+rpk