bama.data

datasets

Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. Bayesian Mediation
Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and
Song et al (2020) <arXiv:2009.11409>,
relies on two Bayesian sparse linear mixed models to simultaneously analyze
a relatively large number of mediators for a continuous exposure and outcome
assuming a small number of mediators are truly active. This sparsity
assumption also allows the extension of univariate mediator analysis by
casting the identification of active mediators as a variable selection
problem and applying Bayesian methods with continuous shrinkage priors on
the effects.

Michael Kleinsasser

bama

High Dimensional Bayesian Mediation Analysis

Alexander Rix

Mike Kleinsasser

Yanyi Song

bama.data function

A data.frame with 1000 observations on 102 variables:<dl>
<dt>y</dt>
<dd>Numeric response variable.</dd><dt>a</dt>
<dd>Numeric exposure variable.</dd><dt>m[1-100]</dt>
<dd>Numeric mediator variables</dd>
</dl>

Format

Synthetic example data for bama — bama.data

A data.frame with 1000 observations on 102 variables:<dl>
<dt>y</dt>
<dd>Numeric response variable.</dd>

<dt>a</dt>
<dd>Numeric exposure variable.</dd>

<dt>m[1-100]</dt>
<dd>Numeric mediator variables</dd>


</dl>

Synthetic example data for bama

bama.data: Synthetic example data for bama

Description

Usage

Arguments

Format