|
Activity Number:
|
233
- Innovative Approaches for High-Dimensional Omics and Neuroimaging Data
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, July 29, 2019 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
International Indian Statistical Association
|
|
Abstract #304204
|
Presentation
|
|
Title:
|
Mediation Analysis for Zero-Inflated Mediators
|
|
Author(s):
|
Zhigang Li* and Janaka Peragaswaththe Liyanage and A. James O'Malley and Susmita Datta
|
|
Companies:
|
University of Florida and University of Florida and Dartmouth College and ASA Committee on Women in Statisitcs
|
|
Keywords:
|
Mediation;
zero-inflated;
microbiome;
causal inference;
longitudinal;
high-dimension
|
|
Abstract:
|
We propose a novel mediation analysis approach under the potential-outcomes framework to model mediators with zero-inflated distributions. This approach can allow a mixture of true zero-value data points and fake zeros that result from data collection procedure. For continuous outcomes, our method is able to decompose the mediation effect into two components that are inherent for zero-inflated mediators: one component is attributable to jump from zero to non-zero state and the other component is attributable to the numeric change on the continuum scale. So the mediation effect is actually a total mediation effect of the two components each of which and the total mediation effect can be estimated and tested. Since there are no existing mediation approaches targeted for zero-inflated mediators, we did a simulation study to assess our approach and show superior performance compared with mediation analyses that simply treat zero-inflated mediators as continuous variables. Traditional linear mediation framework is difficult to accommodate zero-inflated mediators because its mediation effect is based on linear model formulation that does not work well with zero-inflated mediators.
|
Authors who are presenting talks have a * after their name.
