plot,DualSimulationsSummary,missing-method | R Documentation |

This plot method can be applied to `DualSimulationsSummary`

objects in order to summarize them graphically. Possible `type`

of
plots at the moment are those listed in
`plot,SimulationsSummary,missing-method`

plus:

- meanBiomarkerFit
Plot showing the average fitted dose-biomarker curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the

`trueBiomarker`

argument to`summary,DualSimulations-method`

)

You can specify any subset of these in the `type`

argument.

## S4 method for signature 'DualSimulationsSummary,missing' plot( x, y, type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit", "meanBiomarkerFit"), ... )

`x` |
the |

`y` |
missing |

`type` |
the types of plots you want to obtain. |

`...` |
not used |

A single `ggplot`

object if a single plot is
asked for, otherwise a `gridExtra{gTree}`

object.

# Define the dose-grid emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100)) # Initialize the CRM model model <- DualEndpointRW(mu = c(0, 1), Sigma = matrix(c(1, 0, 0, 1), nrow=2), sigma2betaW = 0.01, sigma2W = c(a=0.1, b=0.1), rho = c(a=1, b=1), smooth="RW1") # Choose the rule for selecting the next dose myNextBest <- NextBestDualEndpoint(target=c(0.9, 1), overdose=c(0.35, 1), maxOverdoseProb=0.25) # Choose the rule for the cohort-size mySize1 <- CohortSizeRange(intervals=c(0, 30), cohortSize=c(1, 3)) mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1), cohortSize=c(1, 3)) mySize <- maxSize(mySize1, mySize2) # Choose the rule for stopping myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1), prob=0.5) # only 10 patients here for illustration! myStopping <- myStopping4 | StoppingMinPatients(10) # Choose the rule for dose increments myIncrements <- IncrementsRelative(intervals=c(0, 20), increments=c(1, 0.33)) # Initialize the design design <- DualDesign(model = model, data = emptydata, nextBest = myNextBest, stopping = myStopping, increments = myIncrements, cohortSize = CohortSizeConst(3), startingDose = 3) # define scenarios for the TRUE toxicity and efficacy profiles betaMod <- function (dose, e0, eMax, delta1, delta2, scal) { maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2)) dose <- dose/scal e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2 } trueBiomarker <- function(dose) { betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100) } trueTox <- function(dose) { pnorm((dose-60)/10) } # Draw the TRUE profiles par(mfrow=c(1, 2)) curve(trueTox(x), from=0, to=80) curve(trueBiomarker(x), from=0, to=80) # Run the simulation on the desired design # We only generate 1 trial outcome here for illustration, for the actual study ##For illustration purpose we will use 5 burn-ins to generate 20 samples # this should be increased of course mySims <- simulate(design, trueTox=trueTox, trueBiomarker=trueBiomarker, sigma2W=0.01, rho=0, nsim=1, parallel=FALSE, seed=3, startingDose=6, mcmcOptions = McmcOptions(burnin=5, step=1, samples=20)) # Plot the summary of the Simulations plot(summary(mySims, trueTox = trueTox, trueBiomarker = trueBiomarker))

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