write_pattern function

An internal function to select which type of pattern mixture model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.

write_pattern(
  type,
  dist_e,
  dist_c,
  pe_fixed,
  pc_fixed,
  ind_fixed,
  pe_random,
  pc_random,
  ind_random,
  model_e_random,
  model_c_random,
  d_list,
  d1,
  d2,
  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)
• dist_e: Distribution assumed for the effects. Current available choices 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 choices 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
• ind_fixed: Logical; if TRUE independence between effectiveness and costs is assumed, else correlation is accounted for
• pe_random: Number of random effects for the effectiveness model
• pc_random: Number of random effects for the cost model
• ind_random: Logical; if TRUE independence at the level of the random effects between effectiveness and costs is assumed, else correlation is accounted for
• model_e_random: Random effects formula for the effectiveness model
• model_c_random: Random effects formula for the costs model
• d_list: Number and type of patterns
• d1: Pattern indicator in the control
• d2: Pattern indicator in the intervention
• restriction: type of identifying restriction to be imposed

Examples

# Internal function only
# No examples
#
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