Get the fitted values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Usage

fitGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

# S4 method for class 'ModelTox,Samples,ModelEff,Samples,DataDual'
fitGain(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Arguments

DLEmodel: the DLE pseudo model of ModelTox class object
DLEsamples: the DLE samples of Samples class object
Effmodel: the efficacy pseudo model of ModelEff class object
Effsamples: the efficacy samples of Samples class object
data: the data input of DataDual class object
...: additional arguments for methods
points: at which dose levels is the fit requested? default is the dose grid
quantiles: the quantiles to be calculated (default: 0.025 and 0.975)
middle: the function for computing the middle point. Default: mean

Functions

fitGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples, data = DataDual ): This method returns a data frame with dose, middle, lower, upper quantiles for the gain values obtained given the DLE and the efficacy samples

Examples

##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
#> Used default patient IDs!
#> Used best guess cohort indices!
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)
#> Used default patient IDs!
#> Used best guess cohort indices!

DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)

fitGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
        Effmodel=Effmodel, Effsamples=Effsamples,data=data)
#>    dose    middle      lower     upper
#> 1    25 0.3438477 -0.1699007 0.8544627
#> 2    50 0.5987149  0.2547977 0.9663746
#> 3    75 0.6767000  0.3040261 1.0822600
#> 4   100 0.7013123  0.2964078 1.1623733
#> 5   125 0.7042382  0.2695312 1.2074615
#> 6   150 0.6972572  0.2438631 1.2303285
#> 7   175 0.6855757  0.2240500 1.2498266
#> 8   200 0.6717347  0.2064641 1.2612999
#> 9   225 0.6570509  0.1851396 1.2673728
#> 10  250 0.6422299  0.1680738 1.2696973
#> 11  275 0.6276542  0.1555099 1.2693313
#> 12  300 0.6135277  0.1446233 1.2668871
##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
#> Used default patient IDs!
#> Used best guess cohort indices!
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)
#> Used default patient IDs!
#> Used best guess cohort indices!

DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)

fitGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
        Effmodel=Effmodel, Effsamples=Effsamples,data=data)
#>    dose    middle      lower     upper
#> 1    25 0.3442700 -0.2511183 0.9175569
#> 2    50 0.6051121  0.2599777 1.0301263
#> 3    75 0.6827104  0.3462284 1.0908986
#> 4   100 0.7050072  0.3326112 1.1992161
#> 5   125 0.7057281  0.2824561 1.2516880
#> 6   150 0.6972930  0.2543596 1.3306478
#> 7   175 0.6849992  0.2131238 1.3920869
#> 8   200 0.6712913  0.1845311 1.4412821
#> 9   225 0.6573428  0.1655045 1.4802234
#> 10  250 0.6437208  0.1457352 1.5127283
#> 11  275 0.6306888  0.1270349 1.5067852
#> 12  300 0.6183552  0.1115851 1.4874941