LogisticLogNormalGrouped-class: 'LogisticLogNormalGrouped'

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

LogisticLogNormalGrouped

Description

LogisticLogNormalGrouped is the class for a logistic regression model for both the mono and the combo arms of the simultaneous dose escalation design.

Usage

LogisticLogNormalGrouped(mean, cov, ref_dose = 1)

.DefaultLogisticLogNormalGrouped()

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 continuous covariate is the natural logarithm of the dose x divided by the reference dose x* as in LogisticLogNormal. In addition, I_c is a binary indicator covariate which is 1 for the combo arm and 0 for the mono arm. The model is then defined as:

logit[p(x)] = (alpha0 + I_c * delta0) + (alpha1 + I_c * delta1) * log(x / x*),

where p(x) is the probability of observing a DLT for a given dose x, and delta0 and delta1 are the differences in the combo arm compared to the mono intercept and slope parameters alpha0 and alpha1. The prior is defined as

(alpha0, log(delta0), log(alpha1), log(delta1)) ~ Normal(mean, cov).

Note

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

See Also

ModelLogNormal, LogisticLogNormal.

Examples

my_model <- LogisticLogNormalGrouped(
  mean = c(-0.85, 0, 1, 0),
  cov = diag(1, 4),
  ref_dose = 50
)
my_model

Roche/crmPack documentation built on April 30, 2024, 3:19 p.m.