get_measures

Prepare data frame to plot standardized omitted variable bias
Marginal standardized mean differences and outcome correlation

In the observational study design stage, matching/weighting methods are
conducted. However, when many background variables are present, the decision as to
which variables to prioritize for matching/weighting is not trivial. Thus, the
joint treatment-outcome variable importance plots are created to guide variable
selection. The joint variable importance plots enhance variable comparisons via
unadjusted bias curves derived under the omitted variable bias framework. The
plots translate variable importance into recommended values for tuning parameters
in existing methods. Post-matching and/or weighting plots can also be used to
visualize and assess the quality of the observational study design. The method
motivation and derivation is presented in "Prioritizing Variables for Observational
Study Design using the Joint Variable Importance Plot" by Liao et al. (2024)
<doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel
(2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

Lauren D. Liao

jointVIP

Prioritize Variables with Joint Variable Importance Plot in
Observational Study Design

Samuel D. Pimentel

get_measures function

<dl><dt>object</dt>
<dd>jointVIP object</dd>
<dt>smd</dt>
<dd>calculate standardized mean difference either using <code>cross-sample</code> or <code>pooled</code></dd></dl>

Arguments

Prepare data frame to plot standardized omitted variable bias
Marginal standardized mean differences and outcome correlation — get_measures

<dl>

<dt>object</dt>
<dd>jointVIP object</dd>


<dt>smd</dt>
<dd>calculate standardized mean difference either using <code>cross-sample</code> or <code>pooled</code></dd>

</dl>

get_measures: Prepare data frame to plot standardized omitted variable bias Marginal standardized mean differences and outcome correlation

Description

Usage

Value

Arguments