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ProbitLogNormal

Source: R/Model-class.R
ProbitLogNormal-class.Rd

[Stable]

ProbitLogNormal is the class for probit regression model with a bivariate normal prior on the intercept and log slope.

Usage 

ProbitLogNormal(mean, cov, ref_dose = 1)

.DefaultProbitLogNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose \(x*\) (strictly positive number).

Details

The covariate is the natural logarithm of dose \(x\) divided by a reference dose \(x*\), i.e.: $$probit[p(x)] = alpha0 + alpha1 * log(x/x*),$$ where \(p(x)\) is the probability of observing a DLT for a given dose \(x\). The prior $$(alpha0, log(alpha1)) ~ Normal(mean, cov).$$

Note

The model used in the DualEndpoint classes is an extension of this model: DualEndpoint supports both ProbitNormal (which is not implemented yet) and ProbitLogNormal models through its use_log_dose slot. ProbitLogNormal has no such flag, so always uses log(x/x*)as a covariate in its model. Therefore 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, when use_log_dose = TRUE is used.

Typically, end users will not use the .DefaultProbitLogNormal() function.

See also

ModelLogNormal, LogisticNormal, LogisticLogNormal, LogisticLogNormalSub, ProbitLogNormalRel.

Examples 

my_model <- ProbitLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 7.2
)