df_em_om

Data containing two sources of bias, three known confounders, and
100,000 observations. This data is obtained from <code>df_emc_omc_source</code>
by removing the columns X and Y. The resulting data corresponds
to what a researcher would see in the real-world: a misclassified exposure,
Xstar, and a misclassified outcome, Ystar. As seen in
<code>df_em_om_source</code>, the true, unbiased exposure-outcome
odds ratio = 2.

datasets

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

Simultaneous Multi-Bias Adjustment

df_em_om function

A dataframe with 100,000 rows and 5 columns:<dl>
<dt>Xstar</dt>
<dd>misclassified exposure, 1 = present and 0 = absent</dd><dt>Ystar</dt>
<dd>misclassified outcome, 1 = present and 0 = absent</dd><dt>C1</dt>
<dd>1st confounder, 1 = present and 0 = absent</dd><dt>C2</dt>
<dd>2nd confounder, 1 = present and 0 = absent</dd><dt>C3</dt>
<dd>3rd confounder, 1 = present and 0 = absent</dd>
</dl>

Format

Simulated data with exposure misclassification and outcome misclassification — df_em_om

A dataframe with 100,000 rows and 5 columns:<dl>
<dt>Xstar</dt>
<dd>misclassified exposure, 1 = present and 0 = absent</dd>

<dt>Ystar</dt>
<dd>misclassified outcome, 1 = present and 0 = absent</dd>

<dt>C1</dt>
<dd>1st confounder, 1 = present and 0 = absent</dd>

<dt>C2</dt>
<dd>2nd confounder, 1 = present and 0 = absent</dd>

<dt>C3</dt>
<dd>3rd confounder, 1 = present and 0 = absent</dd>


</dl>

df_em_om: Simulated data with exposure misclassification and outcome misclassification

Description

Usage

Arguments

Format