When a statistical association is observed between an external +exposure (\(X\)) and an individual +outcome (\(Y\)), one or more +intermediate variables ({}) (such as gene expression or epigenetic +changes) may mediate this effect. Identifying and assessing the +mediating role of these variables in the effect of \(X\) on \(Y\) is crucial for deciphering underlying +causal mechanisms in epidemiological and clinical research.
The package hdmax2 is dedicated to high-dimensional
mediation analyses. For mediation analyses, the necessary data includes
one (or more) exposure variable(s), a matrix of potential mediators, and
an outcome variable.
The term “high dimensional” refers to the large quantity of potential mediators among which we seek to identify the actual mediators.
+The fundamental concept behind HDMAX2 methods is to use a latent +factor mixed regression model for estimating unobserved latent factors +while conducting high-dimensional association analysis.
The package hdmax2 is designed to accept
Exposure \(X\) consisting of
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