print.data_observed

Prints a formatted summary of a <code>data_observed</code> object, including:<ul>
<li>The types of biases present</li>
<li>The exposure, outcome, and confounder variables</li>
<li>A preview of the first 5 rows of data</li>
</ul>

internal

Quantify the causal effect of a binary exposure on a binary
outcome with adjustment for multiple biases. The functions can
simultaneously adjust for any combination of uncontrolled confounding,
exposure/outcome misclassification, and selection bias.
The underlying method generalizes the concept of combining inverse
probability of selection weighting with predictive value weighting.
Simultaneous multi-bias analysis can be used to enhance the validity
and transparency of real-world evidence obtained from observational,
longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres,
and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Paul Brendel

multibias

Multiple Bias Analysis in Causal Inference

print.data_observed function

<dl><dt>x</dt>
<dd>A <code>data_observed</code> object</dd>
<dt>...</dt>
<dd>Additional arguments passed to print</dd></dl>

Arguments

Print method for data_observed objects — print.data_observed

<dl>

<dt>x</dt>
<dd>A <code>data_observed</code> object</dd>


<dt>...</dt>
<dd>Additional arguments passed to print</dd>

</dl>

print.data_observed: Print method for data_observed objects

Description

Usage

Value

Arguments