Details
Let rho0 = p(xmin)
be the probability of a DLT at the minimum dose
xmin
, and let gamma
be the dose with target toxicity probability theta
,
i.e. \(p(gamma) = theta\). Then it can easily be shown that the logistic
regression model has intercept
$$[gamma * logit(rho0) - xmin * logit(theta)] / [gamma - xmin]$$
and slope
$$[logit(theta) - logit(rho0)] / [gamma - xmin].$$
The priors are $$gamma ~ Unif(xmin, xmax).$$ and $$rho0 ~ Unif(0, theta).$$
Slots
theta
(
proportion
)
the target toxicity probability.xmin
(
number
)
the minimum of the dose range.xmax
(
number
)
the maximum of the dose range.
Note
The slots of this class, required for creating the model, are the target toxicity, as well as the minimum and maximum of the dose range. Note that these can be different from the minimum and maximum of the dose grid in the data later on.
Typically, end-users will not use the .DefaultLogisticKadane()
function.