df_em_source

Data with complete information on one sources of bias, three known
confounders, and 100,000 observations. This data is used to derive
<code>df_em</code> and can be used to obtain bias parameters for purposes
of validating the simultaneous multi-bias adjustment method with
<code>df_em</code>. With this source data, the fitted regression
logit(P(Y=1)) = &alpha;0 + &alpha;1X + &alpha;2C1 + &alpha;3C2 + &alpha;4C3
shows that 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_em_source function

A dataframe with 100,000 rows and 6 columns:<dl>
<dt>X</dt>
<dd>exposure, 1 = present and 0 = absent</dd><dt>Y</dt>
<dd>true 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><dt>Xstar</dt>
<dd>misclassified exposure, 1 = present and 0 = absent</dd>
</dl>

Format

Data source for df_em — df_em_source

A dataframe with 100,000 rows and 6 columns:<dl>
<dt>X</dt>
<dd>exposure, 1 = present and 0 = absent</dd>

<dt>Y</dt>
<dd>true 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>

<dt>Xstar</dt>
<dd>misclassified exposure, 1 = present and 0 = absent</dd>


</dl>

df_em_source: Data source for `df_em`

Description

Usage

Arguments

Format