# Returns samples from the posterior distributions of each model parameter using WinBUGS If `ff = "logit2"` (i.e. a two-parameter logistic model is used), a matrix of dimensions `production.itr`-by-2 is returned (the first and second columns containing the posterior samples for the intercept and slope parameters respectively). Otherwise, a vector of length `production.itr` is returned. ```r Posterior.R2WinBUGS(tox, notox, sdose, ff, prior.alpha, burnin.itr, production.itr, bugs.directory) ``` ## Arguments - `tox`: A vector of length `k` showing the number of patient who had toxicities at each dose level - `notox`: A vector of length `k` showing the number of patients who did not have toxicities at each dose level - `sdose`: A vector of length `k` listing the standardised doses to be used in the CRM model. - `ff`: A string indicating the functional form of the dose-response curve. Options are - **ht**: 1-parameter hyperbolic tangent - **logit1**: 1-parameter logistic - **power**: 1-parameter power - **logit2**: 2-parameter logistic - `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 1. Gamma(a, b), where a=shape, b=scale: mean=a*b, variance=a*b*b 2. Uniform(a, b), where a=min, b=max 3. Lognormal(a, b), where a=mean on the log scale, b=variance on the log scale 4. Bivariate Lognormal(a, b), where a=mean vector on the log scale, b=Variance-covariance matrix on the log scale. This prior should be used only in conjunction with a two-parameter logistic model. 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, defaults to C:/Program Files/WinBUGS14/ ## Examples ```r ## 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 R2WinBUGS ## Not run: posterior.samples <- Posterior.R2WinBUGS(tox, notox, sdose, ff, prior.alpha , burnin.itr=2000, production.itr=2000, bugs.directory = "C:/Program Files/WinBUGS14/") ## End(Not run) ``` ## References 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](http://www.jstatsoft.org/article/view/v054i13) ## See Also `bcrm`, `find.x` ## Author(s) Michael Sweeting mjs212@medschl.cam.ac.uk (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

Posterior.R2WinBUGS function