CRAN Package Check Results for Package bcrm

Last updated on 2017-02-24 23:52:16.

Check Details

Version: 0.4.6
Flags: --no-stop-on-test-error
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘BRugs’
Flavor: r-devel-macos-x86_64-clang

Version: 0.4.6
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘BRugs’
Flavors: r-patched-solaris-sparc, r-patched-solaris-x86, r-release-osx-x86_64-mavericks

Version: 0.4.6
Check: examples
Result: ERROR
    Running examples in 'bcrm-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: bcrm
    > ### Title: Bayesian Continual Reassessment Method for Phase I
    > ### Dose-Escalation Trials
    > ### Aliases: bcrm
    >
    > ### ** Examples
    >
    > ## 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
    >
    > ## A 1-parameter power model is used, with standardised doses calculated using
    > ## the plug-in prior median
    > ## Prior for alpha is lognormal with mean 0 (on log scale)
    > ## and standard deviation 1.34 (on log scale)
    > ## The recommended dose for the next cohort if posterior mean is used
    > Power.LN.bcrm<-bcrm(stop=list(nmax=18),data=data,p.tox0=p.tox0,dose=dose
    + ,ff="power",prior.alpha=list(3,0,1.34^2),target.tox=target.tox,constrain=FALSE
    + ,sdose.calculate="median",pointest="mean")
    
     Stopping: Reached maximum sample size
    > print(Power.LN.bcrm)
     Estimation method: exact
    
     Model: 1-parameter power
    
     Prior: Lognormal( Mean:0, Variance:1.7956)
    
     Standardised doses (skeleton):
     1 2.5 5 10 15 20 25 30 40 50 75 100 150
    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
     200 250
    0.800 0.900
    
     Unmodified (unconstrained) CRM used
    
     Posterior mean estimate of probability of toxicity used to select next dose
    
     Toxicities observed:
     Doses
     1 2.5 5 10 15 20 25 30 40 50 75 100 150 200 250
     n 3 4 5 4 0 0 2 0 0 0 0 0 0 0 0
     Toxicities 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0
    
     Posterior estimates of toxicity:
     Doses
     1 2.5 5 10 15 20 25 30 40 50
     Mean 0.0702 0.0866 0.1010 0.1130 0.1250 0.1460 0.1650 0.244 0.333 0.467
     SD 0.0558 0.0630 0.0686 0.0731 0.0769 0.0831 0.0879 0.102 0.109 0.108
     Median 0.0561 0.0723 0.0865 0.0995 0.1110 0.1330 0.1530 0.237 0.330 0.471
     Doses
     75 100 150 200 250
     Mean 0.5580 0.641 0.757 0.8650 0.9330
     SD 0.0996 0.088 0.066 0.0398 0.0205
     Median 0.5640 0.648 0.764 0.8700 0.9360
     Doses
    Quantiles 1 2.5 5 10 15 20 25 30 40 50
     2.5% 0.00493 0.00787 0.0110 0.0142 0.0175 0.0244 0.0316 0.0702 0.130 0.249
     25% 0.02860 0.03910 0.0488 0.0579 0.0667 0.0833 0.0990 0.1690 0.255 0.395
     50% 0.05610 0.07230 0.0865 0.0995 0.1110 0.1330 0.1530 0.2370 0.330 0.471
     75% 0.09710 0.11900 0.1380 0.1540 0.1690 0.1960 0.2190 0.3120 0.408 0.544
     97.5% 0.21300 0.24400 0.2690 0.2900 0.3080 0.3400 0.3660 0.4620 0.552 0.668
     Doses
    Quantiles 75 100 150 200 250
     2.5% 0.347 0.450 0.608 0.773 0.886
     25% 0.493 0.586 0.717 0.842 0.922
     50% 0.564 0.648 0.764 0.870 0.936
     75% 0.629 0.704 0.804 0.893 0.948
     97.5% 0.735 0.792 0.865 0.928 0.965
    
     Next recommended dose: 40
    > plot(Power.LN.bcrm)
    >
    > ## 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)
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    > print(Power.LN.bcrm.sim)
    Operating characteristics based on 10 simulations:
    
    
    Sample size 36
    
     Doses
     1 2.5 5 10 15 20 25
     Experimentation proportion 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833
     Recommendation proportion 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
     Doses
     30 40 50 75 100 150 200 250
     Experimentation proportion 0.0833 0.108 0.133 0.075 0.0167 0 0 0
     Recommendation proportion 0.0000 0.100 0.500 0.200 0.2000 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 0.775 0.208 0.0167 0 0
     Recommendation proportion 0.100 0.700 0.2000 0 0
    > plot(Power.LN.bcrm.sim)
    Warning: Ignoring unknown aesthetics: bw
    >
    > ## Comparing this CRM design with the standard 3+3 design
    > ## (only considering the first 12 dose levels)
    > ## Not run:
    > ##D Power.LN.bcrm.compare.sim<-bcrm(stop=list(nmax=36),p.tox0=p.tox0[1:12],dose=dose[1:12]
    > ##D ,ff="power",prior.alpha=list(3,0,1.34^2),target.tox=target.tox,constrain=TRUE
    > ##D ,sdose.calculate="median",pointest="mean",start=1,simulate=TRUE,nsims=50
    > ##D ,truep=p.tox0[1:12],threep3=TRUE)
    > ##D print(Power.LN.bcrm.compare.sim,threep3=TRUE)
    > ##D plot(Power.LN.bcrm.compare.sim,threep3=TRUE)
    > ## End(Not run)
    >
    > ## A 2-parameter model, using priors as specified in Neuenschwander et al 2008.
    > ## Posterior mean used to choose the next dose
    > ## Standardised doses using reference dose, 250mg
    > sdose<-log(dose/250)
    > ## Bivariate lognormal prior for two parameters
    > mu<-c(2.15,0.52)
    > Sigma<-rbind(c(0.84^2,0.134),c(0.134,0.80^2))
    > ## Using rjags (requires JAGS to be installed)
    > TwoPLogistic.mean.bcrm<-bcrm(stop=list(nmax=18),data=data,sdose=sdose
    + ,dose=dose,ff="logit2",prior.alpha=list(4,mu,Sigma),target.tox=target.tox
    + ,constrain=FALSE,pointest="mean",method="rjags")
    Error: .onLoad failed in loadNamespace() for 'rjags', details:
     call: fun(libname, pkgname)
     error: The environment variable JAGS_HOME is set to
    c:/Program Files/JAGS/JAGS-4.0.0
    but no JAGS installation can be found there
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64