A1085
Title: Comparisons of variable selection and 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 has led to many recent advances in mediation analysis, including 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 is also an important step in the mediation analysis. The effect of different variable selection and inference procedures is studied through simulation studies. The simulation settings and the findings are discussed 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.
