HierarchicalModel-class: `HierarchicalModel`

Description

HierarchicalModel is a class for the prototype hierarchical model that links compatible single-agent binary outcome arms and/or TwoDrugsCombo arms.

Usage

HierarchicalModel(
  ...,
  exchangeable_parameters = list(),
  pool_correlations = list(),
  pool_priors = list()
)
.DefaultHierarchicalModel()

Arguments

...: named model objects describing the trial arms.
exchangeable_parameters: a named list describing which parameters are exchangeable across arms. This will be used to define the hierarchical structure of the model. Each list entry contains the arms as names and the parameters to be shared as a string.
pool_correlations: optional named list pairing exactly two scalar entries from exchangeable_parameters into a correlated bivariate hierarchy. Each pair must refer to indices 1 and 2 of the same latent parameter vector in every shared arm. Correlating three or more parameters in one multivariate hierarchy is not supported.
pool_priors: optional named list of hyperprior overrides for entries in exchangeable_parameters. Each entry may contain mu = c(mean, sd) and/or tau = c(meanlog, sdlog).

Slots

models_to_arms: (list)
named list of arm-specific models. Each entry must be either a compatible single-agent binary outcome GeneralModel object using nObs, y, and x inputs, or a TwoDrugsCombo object.

parameter_pools

(list)
named list describing which parameters are exchangeable across arms. Each list entry contains arm names as names and parameter references as values.

pool_correlations

(list)
named list pairing exactly two scalar exchangeable parameter pools that should use a correlated bivariate normal hierarchy.

pool_priors

(list)
named list of pool-specific hyperprior overrides.

Details

The class currently stores the structural pieces from the design prototype as a named list of arm-specific models and a named list of exchangeable parameter pools used to dynamically compile a joint JAGS model.

Examples

Run this code

mono_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 10
)

combo_model <- TwoDrugsCombo(
  single_models = list(
    drug1 = LogisticLogNormal(
      mean = c(-0.85, 1),
      cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
      ref_dose = 10
    ),
    drug2 = LogisticLogNormal(
      mean = c(-0.7, 0.8),
      cov = matrix(c(1.1, -0.3, -0.3, 0.9), nrow = 2),
      ref_dose = 20
    )
  ),
  gamma = 0,
  tau = 1
)

my_model <- HierarchicalModel(
  my_mono = mono_model,
  my_combo = combo_model,
  exchangeable_parameters = list(
    mono_intercept = list(
      my_mono = "alpha0",
      my_combo = "alpha0[1]"
    ),
    mono_slope = list(
      my_mono = "alpha1",
      my_combo = "alpha1[1]"
    )
  )
)

my_model