Represent validation causal data — data

data_validation is one of two different options to represent bias assumptions for bias adjustment. It combines the validation dataframe with specific identification of the appropriate columns for bias adjustment, including: true exposure, true outcome, confounders, misclassified exposure, misclassified outcome, and selection. The purpose of validation data is to use an external data source to transport the necessary causal relationships that are missing in the observed data.

Usage

data_validation(
  data,
  true_exposure,
  true_outcome,
  confounders = NULL,
  misclassified_exposure = NULL,
  misclassified_outcome = NULL,
  selection = NULL
)

Arguments

data: Dataframe of validation data
true_exposure: String name of the column in data corresponding to the true exposure.
true_outcome: String name of the column in data corresponding to the true outcome.
confounders: String name(s) of the column(s) in data corresponding to the confounding variable(s).
misclassified_exposure: String name of the column in data corresponding to the misclassified exposure.
misclassified_outcome: String name of the column in data corresponding to the misclassified outcome.
selection: String name of the column in data corresponding to the selection indicator.

Value

An object of class data_validation containing:

data: A dataframe with the selected columns
true_exposure: The name of the true exposure variable
true_outcome: The name of the true outcome variable
confounders: The name(s) of the confounder variable(s)
misclassified_exposure: The name of the misclassified exposure variable
misclassified_outcome: The name of the misclassified outcome variable
selection: The name of the selection indicator variable

Examples

df <- data_validation(
  data = df_sel_source,
  true_exposure = "X",
  true_outcome = "Y",
  confounders = c("C1", "C2", "C3"),
  selection = "S"
)