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.

You can install from CRAN with:

`install.packages("bcrm")`

Or try the development version from GitHub with:

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

```
library(bcrm)
## Dose-escalation cancer trial example as described in Neuenschwander et al 2008.
## Pre-defined doses
<-c(1,2.5,5,10,15,20,25,30,40,50,75,100,150,200,250)
dose## Pre-specified probabilities of toxicity
## [dose levels 11-15 not specified in the paper, and are for illustration only]
<-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
p.tox00.800,0.900)
,## Data from the first 5 cohorts of 18 patients
<-data.frame(patient=1:18,dose=rep(c(1:4,7),c(3,4,5,4,2)),tox=rep(0:1,c(16,2)))
data## Target toxicity level
<-0.30
target.tox## Random seed set to 12345 for this example
<-12345
seed
## 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)
<-bcrm(stop=list(nmax=36),p.tox0=p.tox0,dose=dose,ff="power"
Power.LN.bcrm.simprior.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
```