prior_selection_long: 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_selection_long(
  type,
  dist_u,
  dist_c,
  pu_fixed,
  pc_fixed,
  zu_fixed,
  zc_fixed,
  model_u_random,
  model_c_random,
  model_mu_random,
  model_mc_random,
  pu_random,
  pc_random,
  zu_random,
  zc_random
)

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_u	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')
pu_fixed	Number of fixed effects for the effectiveness model
pc_fixed	Number of fixed effects for the cost model
zu_fixed	Number of fixed effects or the missingness indicators model for the effectiveness
zc_fixed	Number of fixed effects or the missingness indicators model for the costs
model_u_random	Random effects formula for the effectiveness model
model_c_random	Random effects formula for the costs model
model_mu_random	Random effects formula for the missingness indicators model for the effectiveness
model_mc_random	Random effects formula for the missingness indicators model for the costs
pu_random	Number of random effects for the effectiveness model
pc_random	Number of random effects for the cost model
zu_random	Number of random effects or the missingness indicators model for the effectiveness
zc_random	Number of random effects or the missingness indicators model for the costs

Examples

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