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"
)