Description Usage Arguments Details Author(s) References See Also Examples
Given the prior, functional form of the dose-toxicity model, and data, this function returns the posterior distribution (via either MCMC samples, or posterior summaries) of the CRM model parameter(s)
1 2 3 4 5 | Posterior.exact(tox,notox,sdose,ff,prior.alpha)
Posterior.rjags(tox,notox,sdose,ff,prior.alpha,burnin.itr,production.itr)
Posterior.BRugs(tox,notox,sdose,ff,prior.alpha,burnin.itr,production.itr)
Posterior.R2WinBUGS(tox,notox,sdose,ff,prior.alpha,burnin.itr,production.itr
,bugs.directory)
|
tox |
A vector of length |
notox |
A vector of length |
sdose |
A vector of length |
ff |
A string indicating the functional form of the dose-response curve. Options are
|
prior.alpha |
A list of length 3 containing the distributional information for the prior. The first element is a number from 1-4 specifying the type of distribution. Options are
The second and third elements of the list are the parameters a and b, respectively. |
burnin.itr |
Number of burn-in iterations (default 2000). |
production.itr |
Number of production iterations (default 2000). |
bugs.directory |
Directory that contains the WinBUGS executable if |
Posterior.exact
produces posterior summary statistics of the CRM model parameter(s), and probabilities of toxicity at the dose levels using exact Bayesian computation (integration) of the posterior distribution. If Posterior.BRugs
or Posterior.R2WinBUGS
is specified, then posterior samples of the CRM model parameter(s) is returned by the function.
Michael Sweeting mjs212@medschl.cam.ac.uk (University of Cambridge, UK)
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ## 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
## Lognormal prior
prior.alpha<-list(3,0,1.34^2)
## Power functional form
ff<-"power"
## Standardised doses
sdose<-find.x(ff,p.tox0,alpha=1)
## Posterior distribution of the model parameter using exact computation
post.exact<-Posterior.exact(tox,notox,sdose,ff,prior.alpha)
print(post.exact)
## Posterior distribution of the model parameter using rjags
post.rjags<-Posterior.rjags(tox,notox,sdose,ff,prior.alpha
,burnin.itr=2000,production.itr=2000)
print(mean(post.rjags))
hist(post.rjags)
## Posterior distribution of the model parameter using BRugs (Windows and i386 Linux only)
if(Sys.info()["sysname"] %in% c("Windows","Linux")){
post.BRugs<-Posterior.BRugs(tox,notox,sdose,ff,prior.alpha
,burnin.itr=2000,production.itr=2000)
print(mean(post.BRugs))
hist(post.BRugs)
}
|
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