Standard logistic model with bivariate (log) normal prior with substractive dose standardization
This is the usual logistic regression model with a bivariate normal prior on the intercept and log slope.
The covariate is the dose x minus the reference dose x^{*}:
logit[p(x)] = α + β \cdot (x - x^{*})
where p(x) is the probability of observing a DLT for a given dose x.
The prior is
(α, \log(β)) \sim Normal(μ, Σ)
The slots of this class contain the mean vector and the covariance matrix of the bivariate normal distribution, as well as the reference dose.
meanthe prior mean vector μ
covthe prior covariance matrix Σ
refDosethe reference dose x^{*}
model <- LogisticLogNormalSub(mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
refDose = 50)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.