cea_setup: Create a ceamodel class object.
In bmckay/ceaR: Cost-Effectiveness Analysis Tools for R

Description Usage Arguments Value Methods (by class) Examples

Creates a ceamodel class object that specifies cost and effect data as well as intervention specifiers for a list of individual entities. The model may also specify covariate values to be used in estimating incremental costs and effects.

cea_setup(x, ...)

## Default S3 method:
cea_setup(cst, eff, intv, covt = c(), covt_cst = c(),
  covt_eff = c(), eff_more_better = TRUE, cst_char, eff_char, intv_char,
  covt_char, call.txt, incremental = TRUE, cost_order = TRUE,
  cst_type = "gaussian", eff_type = "gaussian")

## S3 method for class 'data.frame'
cea_setup(cea_data = list(), cst_char, eff_char,
  intv_char, covt_char_vec = c(), covt_cst = c(), covt_eff = c(),
  eff_more_better = TRUE, incremental = TRUE, cost_order = TRUE,
  cst_type = "gaussian", eff_type = "gaussian")

## S3 method for class 'formula'
cea_setup(formula_cea = formula, intv, cea_data = list(),
  eff_more_better = TRUE, incremental = TRUE, cost_order = TRUE,
  cst_type = "gaussian", eff_type = "gaussian")

`x`	an R object.
`cst`	A vector of cost values.
`eff`	A vector of effect values.
`intv`	A vector of intervention or program assignment/membership. The vectors cst, eff, and intv must have the same length, greater than one.
`covt`	If provided, a vector (or matrix) of covariate values. covt must have the same number of rows as cst and eff. The number of columns is equal to the number of unique covariates.
`covt_cst`	If provided, a vector of integers. The integers correspond to column numbers of covt for which the covariates will be used for costs.
`covt_eff`	If provided, a vector of integers. The integers correspond to column numbers of covt for which the covariates will be used for effects.
`eff_more_better`	If TRUE, a greater value for effects indicates a better outcome. If FALSE, a smaller value for effects indicates a better outcome. Default is TRUE.
`cst_char`	A character string representing the preferred name of the cost variable (values in cst).
`eff_char`	A character string representing the preferred name of the effect variable (values in eff).
`intv_char`	A character string representing the preferred name of the intervention variable (values in intv).
`covt_char`	A character string (or vector of character strings) representing the preferred name of the covariate variables. The number of strings provided should equal the number of columns in covt.
`call.txt`	Only supplied when one of the cea_setup methods calls this default function.
`incremental`	Defaults to TRUE and runs incremental analysis upon setup.
`cost_order`	If true, the order of options in an ICER table will be by increasing average cost. If false, the order of options in an ICER table will be by increasing average effect.
`cst_type`	Defaults to linear regression (gaussian) but can be set to any family object available to glm.
`eff_type`	Defaults to linear regression (gaussian) but can be set to any family object available to glm.

An object of class "ceamodel".

default: Default S3 method
data.frame: S3 method for class 'data.frame'
formula: S3 method for class 'formula'

## Data Frame examples: basic, male as covariate for effects only
ceamodel <- cea_setup(clintrial_cea, "cost", "qaly", "treat")
ceamodel <- cea_setup(clintrial_cea, "cost", "qaly", "treat", 
covt_eff = "male")

## Formula examples: basic, male as covariate for effects only
ceamodel <- cea_setup(cost | qaly ~ 1, "treat", clintrial_cea)
ceamodel <- cea_setup(cost | qaly ~ 1 | male, "treat", clintrial_cea)