Dual endpoint model
muFor the probit toxicity model, mu contains the prior mean
vector
SigmaFor the probit toxicity model, contains the prior covariance matrix
sigma2betaWFor the biomarker model, contains the prior variance
factor of the random walk prior. If it is not a single number, it can also
contain a vector with elements a and b for the inverse-gamma prior
on sigma2betaW.
sigma2WEither a fixed value for the biomarker variance, or a vector
with elements a and b for the inverse-gamma prior parameters.
rhoEither a fixed value for the correlation (between -1 and 1), or a
vector with elements a and b for the Beta prior on the
transformation kappa = (rho + 1) / 2, which is in (0, 1). For example,
a=1,b=1 leads to a uniform prior on rho.
useRW1for specifying the random walk prior on the biomarker level: if
TRUE, RW1 is used, otherwise RW2.
useFixeda list with logical value for each of the three parameters
sigma2betaW, sigma2W and rho indicating whether
a fixed value is used or not.
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