B0736
Title: Comparisons of inference methods in high-dimensional mediation analysis
Authors: Xizhen Cai - Williams College (United States) [presenting]
Yeying Zhu - University of Waterloo (Canada)
Yuan Huang - Yale University (United States)
Abstract: Mediation analysis is a framework to understand how a treatment affects the outcome through intermediate variables, namely mediators. Over the past decades, large and high-dimensional datasets have become easily stored and publicly available. This leads to many recent advances in mediation analysis in developing models to fit more complex data structures and methods for mediator selections in high-dimensional settings. The statistical inference procedure following the mediator selection also serves as an essential step in the mediation analysis. We study the effect of applying different inference procedures after the mediator selection and perform simulation studies to further compare these procedures. We will discuss our simulation settings and the findings to provide guidelines that help distinguish among various approaches, highlight the advantages and disadvantages of each, and identify ones that perform better in certain scenarios.
