summary.data_observed

Provides a statistical summary of the observed data by fitting either:<ul>
<li>A logistic regression model for binary outcomes</li>
<li>A linear regression model for continuous outcomes</li>
</ul>The model includes the exposure and all confounders as predictors.
For binary outcomes, estimates are exponentiated to show odds ratios.

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

summary.data_observed function

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

Arguments

Provides a statistical summary of the observed data by fitting either:<ul>
<li>A logistic regression model for binary outcomes</li>
<li>A linear regression model for continuous outcomes</li>
</ul>

The model includes the exposure and all confounders as predictors.
For binary outcomes, estimates are exponentiated to show odds ratios.

Summary method for data_observed objects — summary.data_observed

<dl>

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


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

</dl>

summary.data_observed: Summary method for data_observed objects

Description

Usage

Value

Arguments