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Fit a covariate regression model

covariate_model.Rd

Fit a covariate regression model using gemtc.

Usage

covariate_model(
  configured_data,
  covariate_value,
  regressor_type,
  covariate_model_output = NULL,
  n_adapt = 5000,
  n_iter = 20000,
  async = FALSE
)

Arguments

configured_data

list. Input dataset created by setup_configure() or setup_exclude()

covariate_value

numeric. The value at which to fit the model. Must be greater than or equal to the minimum value and less than or equal to the maximum value in configured_data

regressor_type

character. Type of regression coefficient, either shared, unrelated, or exchangeable

covariate_model_output

list. The output of the function. Default NULL. When supplied, only the output is recalculated for a given covariate value, rather than refitting the model.

n_adapt

numeric. Number of adaptation iterations. Defaults to 5000 and can normally be left unchanged

n_iter

numeric. Number of simulation iterations. Defaults to 20000 and can normally be left unchanged

async

Whether or not the function is being used asynchronously. Default FALSE

Value

List of gemtc related output:

mtcResults

model object from gemtc::mtc.run() carried through (needed to match existing code)

mtcRelEffects

data relating to presenting relative effects

rel_eff_tbl

table of relative effects for each comparison

covariate_value

The covariate value originally passed into this function

reference_treatment

character. The reference_treatmentfrom configured_data

comparator_names

Vector containing the names of the comparators

a

text output stating whether fixed or random effects

sumresults

summary output of relative effects

dic

data frame of model fit statistics

cov_value_sentence

text output stating the value for which the covariate has been set to for producing output

slopes

named list of slopes for the regression equations (unstandardised - equal to one 'increment')

intercepts

named list of intercepts for the regression equations at covariate_value

outcome

character. The outcome from configured_data

outcome_measure

character. The outcome_measurefrom configured_data

effects

character. The effects from configured_data

mtcNetwork

The network object from GEMTC

covariate_min

Vector of minimum covariate values directly contributing to the regression

covariate_max

Vector of maximum covariate values directly contributing to the regression

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

# initial model
fitted_covariate_model <- covariate_model(configured_data = configured_data,
                                          covariate_value = 98,
                                          regressor_type = "shared",
                                          n_adapt = 100,
                                          n_iter = 100)

# updated for new covariate value
updated_covariate_model <- covariate_model(configured_data = configured_data,
                                          covariate_value = 97,
                                          regressor_type = "shared",
                                          covariate_model_output = fitted_covariate_model,
                                          n_adapt = 100,
                                          n_iter = 100)