DualEndpointBeta-class | R Documentation |

This class extends the `DualEndpoint`

class. Here the
dose-biomarker relationship *f(x)* is modelled by a parametric, rescaled
beta density function:

*f(x) = E_{0} + (E_{max} - E_{0}) * Beta(δ_{1}, δ_{2}) *
(x/x^{*})^{δ_{1}} * (1 - x/x^{*})^{δ_{2}}*

where *x^{*}* is the maximum dose (end of the dose range to be
considered), *δ_{1}* and *δ_{2}* are the two beta
parameters, and *E_{0}* and *E_{max}* are the minimum and maximum
levels, respectively. For ease of interpretation, we parametrize with
*δ_{1}* and the mode of the curve instead, where

*mode = δ_{1} / (δ_{1} + δ_{2}),*

and multiplying this with *x^{*}* gives the mode on the dose grid.

All parameters can currently be assigned uniform distributions or be fixed
in advance. Note that `E0`

and `Emax`

can have negative values or uniform
distributions reaching into negative range, while `delta1`

and `mode`

must be positive or have uniform distributions in the positive range.

`E0`

either a fixed number or the two uniform distribution parameters

`Emax`

either a fixed number or the two uniform distribution parameters

`delta1`

either a fixed number or the two uniform distribution parameters

`mode`

either a fixed number or the two uniform distribution parameters

`refDoseBeta`

the reference dose

*x^{*}*(note that this is different from the`refDose`

in the inherited`DualEndpoint`

model)

model <- DualEndpointBeta(E0 = c(0, 100), Emax = c(0, 500), delta1 = c(0, 5), mode = c(1, 15), refDose=10, useLogDose=TRUE, refDoseBeta = 1000, mu = c(0, 1), Sigma = matrix(c(1, 0, 0, 1), nrow=2), sigma2W = c(a=0.1, b=0.1), rho = c(a=1, b=1))

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