Configure the analysis — setup_configure • metainsight

Checks the connectivity of the loaded data and converts it into formats for later analyses. Conducts a frequentist analysis using netmeta::netmeta(). The output can be passed to many other functions - all summary_ and freq_ functions and bayes_model(), baseline_model() and covariate_model().

Usage

setup_configure(
  loaded_data,
  reference_treatment,
  effects,
  outcome_measure,
  ranking_option,
  seed,
  logger = NULL
)

Arguments

loaded_data: list. Output from setup_load()
reference_treatment: character. The reference treatment of the dataset
effects: character. Type of model to fit, either random or fixed
outcome_measure: character. Outcome measure of the dataset. Either OR, RR or RD when outcome is binary or MD or SMD when outcome is continuous
ranking_option: character. good if the treatment effect is desirable, else bad
seed: numeric. Seed used to fit the models.
logger: Stores all notification messages to be displayed in the Log Window. Insert the logger reactive list here for running in shiny, otherwise leave the default NULL

Value

configured_data containing:

treatments

dataframe. Treatment names and IDs

reference_treatment

character. The selected reference treatment

disconnected_indices

vector. Indices of studies that are not connected to the main network

connected_data

dataframe. A subset of the data containing only connected studies

non_covariate_data

dataframe. The uploaded data with covariates removed

covariate

A list containing these items if covariate data exists or else empty:

column: character. Name of the column containing covariate data
name: character. Name of the covariate
type: character. Whether the covariate is binary or continuous

freq

list. Processed data for frequentist analyses created by frequentist()

outcome

character. Whether the data is binary or continuous

outcome_measure

character. Outcome measure of the dataset.

effects

character. Whether the models are fixed or random effects

ranking_option

character. Whether higher values in the data are good or bad

seed

numeric. A seed value to be passed to models

Examples

minimal_data_path <- system.file("extdata", "continuous_minimal.csv", package = "metainsight")
loaded_data <- setup_load(data_path = minimal_data_path,
                          outcome = "continuous")

configured_data <- setup_configure(loaded_data = loaded_data,
                                   reference_treatment = "the Great",
                                   effects = "random",
                                   outcome_measure = "MD",
                                   ranking_option = "good",
                                   seed = 123)