Description Usage Arguments Value Author(s) See Also Examples

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).

1 2 3 4 |

`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`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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)
``` |

DoseFinding documentation built on Jan. 5, 2018, 1:05 a.m.

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