df_om_sel

Data containing two sources of bias, a known confounder, and
100,000 observations. This data is obtained by sampling with replacement
with probability = S from <code>df_om_sel_source</code> then removing the
columns Y and S. The resulting data corresponds to what a
researcher would see in the real-world: a misclassified outcome,
Ystar, and missing data for those not selected into the study
(S=0). As seen in <code>df_om_sel_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

Multiple Bias Analysis in Causal Inference

df_om_sel function

A dataframe with 100,000 rows and 5 columns:<dl>
<dt>X</dt>
<dd>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 outcome misclassification and selection bias — df_om_sel

A dataframe with 100,000 rows and 5 columns:<dl>
<dt>X</dt>
<dd>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_om_sel: Simulated data with outcome misclassification and selection bias

Description

Usage

Arguments

Format