data_validation {multibias} | R Documentation |
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 |
true_outcome |
String name of the column in |
confounders |
String name(s) of the column(s) in |
misclassified_exposure |
String name of the column in |
misclassified_outcome |
String name of the column in |
selection |
String name of the column in |
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"
)