Target Doses {DoseFinding} | 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_\Delta = \min \{x|f(x) > f(0)+\Delta\}`

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

`TD_\Delta = \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).

`ED_p=\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). |

`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

### See Also

`Mods`

, `fitMod`

, `bFitMod`

, `drmodels`

### 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)
```

*DoseFinding*version 1.1-1 Index]