Bayesian Continual Reassessment Method for Phase I Dose-Escalation Trials.

bcrm

Implements a wide variety of one and two-parameter Bayesian CRM designs. The program can run interactively, allowing the user to enter outcomes after each cohort has been recruited, or via simulation to assess operating characteristics.

Installation

You can install from CRAN with:

install.packages("bcrm")

Or try the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("mikesweeting/bcrm")

Example

library(bcrm)

## 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
data<-data.frame(patient=1:18,dose=rep(c(1:4,7),c(3,4,5,4,2)),tox=rep(0:1,c(16,2)))
## Target toxicity level
target.tox<-0.30
## Random seed set to 12345 for this example
seed<-12345

## Simulate 10 replicate trials of size 36 (cohort size 3) using this design 
## with constraint (i.e. no dose-skipping) and starting at lowest dose
## True probabilities of toxicity are set to pre-specified probabilities (p.tox0) 
Power.LN.bcrm.sim<-bcrm(stop=list(nmax=36),p.tox0=p.tox0,dose=dose,ff="power"
  ,prior.alpha=list(3,0,1.34^2),target.tox=target.tox,constrain=TRUE
  ,sdose.calculate="median",pointest="mean",start=1,simulate=TRUE,nsims=10,truep=p.tox0, seed=seed)
#> Simulated trial: 10
print(Power.LN.bcrm.sim)
#> Operating characteristics based on  10  simulations: 
#>  
#>               
#> Sample size 36
#> 
#>                             Doses
#>                              No dose      1    2.5      5     10     15
#>   Experimentation proportion      NA 0.0833 0.0833 0.0833 0.0833 0.0833
#>   Recommendation proportion        0 0.0000 0.0000 0.0000 0.0000 0.0000
#>                             Doses
#>                                  20     25     30    40    50     75
#>   Experimentation proportion 0.0833 0.0833 0.0833 0.167 0.117 0.0417
#>   Recommendation proportion  0.0000 0.0000 0.0000 0.200 0.600 0.2000
#>                             Doses
#>                                  100 150 200 250
#>   Experimentation proportion 0.00833   0   0   0
#>   Recommendation proportion  0.00000   0   0   0
#> 
#>                             Probability of DLT
#>                              [0,0.2] (0.2,0.4] (0.4,0.6] (0.6,0.8] (0.8,1]
#>   Experimentation proportion   0.833     0.158   0.00833         0       0
#>   Recommendation proportion    0.200     0.800   0.00000         0       0