Probit model with bivariate log normal prior
This is probit regression model with a bivariate normal prior on the intercept and log slope. The covariate is the dose x itself, potentially divided by a reference dose x^{*}, or the logarithm of it:
probit[p(x)] = alpha + beta * x/x*
or
probit[p(x)] = alpha + beta * log(x/x*)
in case that the option useLogDose is TRUE.
Here p(x) is the probability of observing a DLT for a given dose
x.
The prior is
(alpha, beta) ~ Normal(mu, Sigma)
The slots of this class contain the mean vector and the covariance matrix of the bivariate normal distribution, as well as the reference dose. Note that the parametrization inside the class uses alpha0 and alpha1.
This model is also used in the DualEndpoint classes,
so this class can be used to check the prior assumptions on the dose-toxicity
model - even when sampling from the prior distribution of the dual endpoint model
is not possible.
muthe prior mean vector μ
Sigmathe prior covariance matrix Σ
refDosethe reference dose x^{*}
useLogDoseshould the log of (standardized) dose be used?
model <- ProbitLogNormal(mu = c(-0.85, 1),
Sigma = matrix(c(1, -0.5, -0.5, 1), nrow = 2))
## we can also specify a reference dose, and use a log transformation of
## standardized dose in the model:
model <- ProbitLogNormal(mu = c(-0.85, 1),
Sigma = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
refDose = 7.2,
useLogDose=TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.