multibias (version 1.7.1)

data_validation: Represent validation causal data

Description

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.

Examples

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