R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Calculate dose estimates for a fitted dose-response model...

Target Doses

R Documentation

Calculate dose estimates for a fitted dose-response model (via `fitMod` or `bFitMod`) or a `Mods` object.

Description

The TD (target dose) is defined as the dose that achieves a target effect of Delta over placebo (if there are multiple such doses, the smallest is chosen):

TD = min {x|f(x) > f(0)+Delta}

If a decreasing trend is beneficial the definition of the TD is

TD = min {x|f(x) < f(0)-Delta}

When Delta is the clinical relevance threshold, then the TD is similar to the usual definition of the minimum effective dose (MED).

The ED (effective dose) is defined as the dose that achieves a certain percentage p of the full effect size (within the observed dose-range!) over placebo (if there are multiple such doses, the smallest is chosen).

EDp=min {x|f(x) > f(0) + p(f(dmax)-f(0))}

Note that this definition of the EDp is different from traditional definition based on the Emax model, where the EDp is defined relative to the asymptotic maximum effect (rather than the maximum effect in the observed dose-range).

Usage

TD(object, Delta, TDtype = c("continuous", "discrete"),
   direction = c("increasing", "decreasing"), doses)

ED(object, p, EDtype = c("continuous", "discrete"), doses)

Arguments

`object`	An object of class c(Mods, fullMod), DRMod or bFitMod
`Delta, p`	Delta: The target effect size use for the target dose (TD) (Delta should be > 0). p: The percentage of the dose to use for the effective dose.
`TDtype, EDtype`	character that determines, whether the dose should be treated as a continuous variable when calculating the TD/ED or whether the TD/ED should be calculated based on a grid of doses specified in doses
`direction`	Direction to be used in defining the TD. This depends on whether an increasing or decreasing of the response variable is beneficial.
`doses`	Dose levels to be used, this needs to include placebo, TDtype or EDtype are equal to "discrete".

Value

Returns the dose estimate

Author(s)

Bjoern Bornkamp

Examples

## example for creating a "full-model" candidate set placebo response
## and maxEff already fixed in Mods call
doses <- c(0, 10, 25, 50, 100, 150)
fmodels <- Mods(linear = NULL, emax = 25,
                   logistic = c(50, 10.88111), exponential = 85,
                   betaMod = rbind(c(0.33, 2.31), c(1.39, 1.39)),
                   linInt = rbind(c(0, 1, 1, 1, 1),
                                  c(0, 0, 1, 1, 0.8)), 
                   doses=doses, placEff = 0, maxEff = 0.4,
                   addArgs=list(scal=200))

## calculate doses giving an improvement of 0.3 over placebo
TD(fmodels, Delta=0.3)
## discrete version
TD(fmodels, Delta=0.3, TDtype = "discrete", doses=doses)
## doses giving 50% of the maximum effect
ED(fmodels, p=0.5)
ED(fmodels, p=0.5, EDtype = "discrete", doses=doses)

plot(fmodels, plotTD = TRUE, Delta = 0.3)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DoseFinding)
Loading required package: lattice
Loading required package: mvtnorm
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DoseFinding/targdose.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Target Doses
> ### Title: Calculate dose estimates for a fitted dose-response model (via
> ###   'fitMod' or 'bFitMod') or a 'Mods' object.
> ### Aliases: TD ED
> 
> ### ** Examples
> 
> ## example for creating a "full-model" candidate set placebo response
> ## and maxEff already fixed in Mods call
> doses <- c(0, 10, 25, 50, 100, 150)
> fmodels <- Mods(linear = NULL, emax = 25,
+                    logistic = c(50, 10.88111), exponential = 85,
+                    betaMod = rbind(c(0.33, 2.31), c(1.39, 1.39)),
+                    linInt = rbind(c(0, 1, 1, 1, 1),
+                                   c(0, 0, 1, 1, 0.8)), 
+                    doses=doses, placEff = 0, maxEff = 0.4,
+                    addArgs=list(scal=200))
> 
> ## calculate doses giving an improvement of 0.3 over placebo
> TD(fmodels, Delta=0.3)
     linear        emax    logistic exponential    betaMod1    betaMod2 
 112.500000   45.000000   62.095220  130.265330    4.880978   56.762044 
    linInt1     linInt2 
  21.250000   43.750000 
> ## discrete version
> TD(fmodels, Delta=0.3, TDtype = "discrete", doses=doses)
     linear        emax    logistic exponential    betaMod1    betaMod2 
        150          50         100         150          10         100 
    linInt1     linInt2 
         25          50 
> ## doses giving 50% of the maximum effect
> ED(fmodels, p=0.5)
     linear        emax    logistic exponential    betaMod1    betaMod2 
  75.000000   18.750000   50.215409  104.517639    1.255838   37.337384 
    linInt1     linInt2 
         NA          NA 
> ED(fmodels, p=0.5, EDtype = "discrete", doses=doses)
     linear        emax    logistic exponential    betaMod1    betaMod2 
        100          25         100         150          10          50 
    linInt1     linInt2 
         25          50 
> 
> plot(fmodels, plotTD = TRUE, Delta = 0.3)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>

Calculate dose estimates for a fitted dose-response model (via fitMod or bFitMod) or a Mods object.