LogisticLogNormalGrouped

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
#> An object of class "LogisticLogNormalGrouped"
#> Slot "params":
#> An object of class "ModelParamsNormal"
#> Slot "mean":
#> [1] -0.85  0.00  1.00  0.00
#> 
#> Slot "cov":
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> Slot "prec":
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> 
#> Slot "ref_dose":
#> An object of class "positive_number"
#> [1] 50
#> 
#> Slot "datamodel":
#> function() {
#>       for (i in 1:nObs) {
#>         logit(p[i]) <- (alpha0 + is_combo[i] * delta0) +
#>           (alpha1 + is_combo[i] * delta1) * log(x[i] / ref_dose)
#>         y[i] ~ dbern(p[i])
#>       }
#>     }
#> <bytecode: 0x55c95740c0d8>
#> <environment: 0x55c958bcc998>
#> 
#> Slot "priormodel":
#> function() {
#>       theta ~ dmnorm(mean, prec)
#>       alpha0 <- theta[1]
#>       delta0 <- exp(theta[2])
#>       alpha1 <- exp(theta[3])
#>       delta1 <- exp(theta[4])
#>     }
#> <bytecode: 0x55c956ff7bb8>
#> <environment: 0x55c958bcc998>
#> 
#> Slot "modelspecs":
#> function(group, from_prior) {
#>       ms <- list(
#>         mean = params@mean,
#>         prec = params@prec
#>       )
#>       if (!from_prior) {
#>         ms$ref_dose <- ref_dose
#>         ms$is_combo <- as.integer(group == "combo")
#>       }
#>       ms
#>     }
#> <bytecode: 0x55c9572462d8>
#> <environment: 0x55c958bcc998>
#> 
#> Slot "init":
#> function() {
#>       list(theta = c(0, 1, 1, 1))
#>     }
#> <bytecode: 0x55c956ab1200>
#> <environment: 0x55c958bcc998>
#> 
#> Slot "datanames":
#> [1] "nObs" "y"    "x"   
#> 
#> Slot "datanames_prior":
#> character(0)
#> 
#> Slot "sample":
#> [1] "alpha0" "delta0" "alpha1" "delta1"
#>