  Various methods have emerged for conducting mediation analyses with multiple
correlated mediators, each with distinct strengths and limitations. However, a
comparative evaluation of these methods is lacking, providing the motivation
for this paper. This study examines six mediation analysis methods for multiple
correlated mediators that provide insights to the contributors for health
disparities. We assessed the performance of each method in identifying joint or
path-specific mediation effects in the context of binary outcome variables
varying mediator types and levels of residual correlation between mediators.
Through comprehensive simulations, the performance of six methods in estimating
joint and/or path-specific mediation effects was assessed rigorously using a
variety of metrics including bias, mean squared error, coverage and width of
the 95$\%$ confidence intervals. Subsequently, these methods were applied to
the REasons for Geographic And Racial Differences in Stroke (REGARDS) study,
where differing conclusions were obtained depending on the mediation method
employed. This evaluation provides valuable guidance for researchers grappling
with complex multi-mediator scenarios, enabling them to select an optimal
mediation method for their research question and dataset.
