TITELogisticLogNormal — TITELogisticLogNormal-class • crmPack

TITELogisticLogNormal is the class for TITE-CRM based on a logistic regression model using a bivariate normal prior on the intercept and log slope parameters.

This class inherits from the LogisticLogNormal.

Usage

TITELogisticLogNormal(weight_method = "linear", ...)

.DefaultTITELogisticLogNormal()

Arguments

weight_method

(string)
see the slot description.

...

Arguments passed on to LogisticLogNormal

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

Basically, the adaptive function allocates more weight to each record than the linear function when DLTs are observed early and less weight when DLTs are observed late. When DLT times are evenly distributed both weights are similar. In addition, with more DLTs, the adaptive weights become more extreme and different from the linear weights.

Slots

weight_method: (string)
the weight function method: either linear or adaptive; see Liu et al. (2013) .

Note

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

References

Liu S, Yin G, Yuan Y (2013). “Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity.” The Annals of Applied Statistics, 7(4), 2138–2156. doi:10.1214/13-AOAS661 .

Examples

my_model <- TITELogisticLogNormal(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 1,
  weight_method = "linear"
)

my_model1 <- TITELogisticLogNormal(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 1,
  weight_method = "adaptive"
)

`TITELogisticLogNormal`