data_read_selection: A function to read and re-arrange the data in different ways

In missingHE: Missing Outcome Data in Health Economic Evaluation

A function to read and re-arrange the data in different ways

Description

This internal function imports the data and outputs only those variables that are needed to run the model according to the information provided by the user.

Usage

data_read_selection(
  data,
  model.eff,
  model.cost,
  model.me,
  model.mc,
  type,
  center
)

Arguments

data	A data frame in which to find variables supplied in model.eff and model.cost. Among these, effectiveness, cost and treatment indicator (only two arms) variables must always be provided and named 'e', 'c' and 't' respectively.
model.eff	A formula expression in conventional R linear modelling syntax. The response must be a health economics effectiveness outcome ('e') whose name must correspond to that used in data, and any covariates are given on the right-hand side. If there are no covariates, specify 1 on the right hand side. By default, covariates are placed on the "location" parameter of the distribution through a linear model. Random effects can also be specified for each model parameter.
model.cost	A formula expression in conventional R linear modelling syntax. The response must be a health economics cost outcome ('c') whose name must correspond to that used in data, and any covariates are given on the right-hand side. If there are no covariates, specify 1 on the right hand side. By default, covariates are placed on the "location" parameter of the distribution through a linear model. Random effects can also be specified for each model parameter.
model.me	A formula expression in conventional R linear modelling syntax. The response must be indicated with the term 'me'(missing effects) and any covariates used to estimate the probability of missing effects are given on the right-hand side. If there are no covariates, specify 1 on the right hand side. By default, covariates are placed on the "probability" parameter for the missing effects through a logistic-linear model. Random effects can also be specified for each model parameter.
model.mc	A formula expression in conventional R linear modelling syntax. The response must be indicated with the term 'mc'(missing costs) and any covariates used to estimate the probability of missing costs should be given on the right-hand side. If there are no covariates, specify 1 on the right hand side. By default, covariates are placed on the "probability" parameter for the missing costs through a logistic-linear model. Random effects can also be specified for each model parameter.
type	Type of missingness mechanism assumed. Choices are Missing At Random (MAR) and Missing Not At Random (MNAR).
center	Logical. If center is TRUE all the covariates in the model are centered.

Examples

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