Target Doses {DoseFinding} | R Documentation |

`fitMod`

or `bFitMod`

) or a `Mods`

object.
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).

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

`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). |

`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". |

Returns the dose estimate

Bjoern Bornkamp

`Mods`

, `fitMod`

, `bFitMod`

, `drmodels`

## 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)

[Package *DoseFinding* version 1.0-1 Index]