Given a posterior (or prior) sample of the parameters, this function inverts the given functional form to obtain samples from the MTD distribution.
find.x(ff, ptox, alpha)
A string indicating the functional form of the dose-response curve. Options are
The required probability of DLT. For example, if the MTD distribution is sought then set
A sample from the posterior (or prior) distribution of the parameter(s).
Given a posterior (or prior) sample of the parameters, this function inverts the given functional form to obtain samples from the MTD distribution or any other targeted quantile.
Michael Sweeting email@example.com (University of Cambridge, UK), drawing on code originally developed by J. Jack Lee and Nan Chen, Department of Biostatistics, the University of Texas M. D. Anderson Cancer Center
Sweeting M., Mander A., Sabin T. bcrm: Bayesian Continual Reassessment Method Designs for Phase I Dose-Finding Trials. Journal of Statistical Software (2013) 54: 1–26. http://www.jstatsoft.org/article/view/v054i13
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## Dose-escalation cancer trial example as described in Neuenschwander et al 2008. ## Pre-defined doses dose<-c(1,2.5,5,10,15,20,25,30,40,50,75,100,150,200,250) ## Pre-specified probabilities of toxicity ## [dose levels 11-15 not specified in the paper, and are for illustration only] p.tox0<-c(0.010,0.015,0.020,0.025,0.030,0.040,0.050,0.100,0.170,0.300,0.400,0.500,0.650 ,0.800,0.900) ## Data from the first 5 cohorts of 18 patients tox<-c(0,0,0,0,0,0,2,0,0,0,0,0,0,0,0) notox<-c(3,4,5,4,0,0,0,0,0,0,0,0,0,0,0) ## Target toxicity level target.tox<-0.30 ## Prior distribution for the MTD given a lognormal(0,1.34^2) distribution for alpha ## and a power model functional form prior.alpha<-list(3,0,1.34^2) ff<-"power" samples.alpha<-getprior(prior.alpha,2000) mtd<-find.x(ff,target.tox,alpha=samples.alpha) hist(mtd) ## Standardised doses sdose<-find.x(ff,p.tox0,alpha=1) ## Posterior distribution of the MTD (on standardised dose scale) using data ## from the cancer trial described in Neuenschwander et al 2008. ## Using rjags posterior.samples<-Posterior.rjags(tox,notox,sdose,ff,prior.alpha ,burnin.itr=2000,production.itr=2000) posterior.mtd<-find.x(ff,target.tox,alpha=posterior.samples) hist(posterior.mtd)