prior_pattern: An internal function to change the hyperprior parameters in...

In missingHE: Missing Outcome Data in Health Economic Evaluation

An internal function to change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumed

Description

This function modifies default hyper prior parameter values in the type of selection model selected according to the type of missingness mechanism and distributions for the outcomes assumed.

Usage

prior_pattern(
  type,
  dist_e,
  dist_c,
  pe_fixed,
  pc_fixed,
  model_e_random,
  model_c_random,
  pe_random,
  pc_random,
  d_list,
  restriction
)

Arguments

type	Type of missingness mechanism assumed. Choices are Missing At Random (MAR), Missing Not At Random for the effects (MNAR_eff), Missing Not At Random for the costs (MNAR_cost), and Missing Not At Random for both (MNAR). For a complete list of all available hyper parameters and types of models see the manual.
dist_e	distribution assumed for the effects. Current available chocies are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'), Weibull ('weibull'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('nbinom') or Bernoulli ('bern')
dist_c	Distribution assumed for the costs. Current available chocies are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm')
pe_fixed	Number of fixed effects for the effectiveness model
pc_fixed	Number of fixed effects for the cost model
model_e_random	Random effects formula for the effectiveness model
model_c_random	Random effects formula for the costs model
pe_random	Number of random effects for the effectiveness model
pc_random	Number of random effects for the cost model
d_list	a list of the number and types of patterns in the data
restriction	type of identifying restriction to be imposed

Examples

#Internal function only
#no examples
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missingHE documentation built on March 31, 2023, 10:27 p.m.