Causal Mediation Analysis for Zero-inflated Mixture Mediators

TL;DR

This paper introduces a novel statistical method for causal mediation analysis involving zero-inflated mixture mediators, effectively modeling complex mediator data and distinguishing true zeros from false zeros.

Contribution

It develops a flexible finite mixture distribution approach for zero-inflated mediators and derives a two-part mediation effect, advancing mediation analysis techniques.

Findings

Method accurately captures zero-inflated mediator data.

Simulation studies demonstrate improved performance over standard methods.

Application to neuroscience data shows practical utility.

Abstract

Causal mediation analysis is an important statistical tool to quantify effects transmitted by intermediate variables from a cause to an outcome. There is a gap in mediation analysis methods to handle mixture mediator data that are zero-inflated with multi-modality and atypical behaviors. We propose an innovative way to model zero-inflated mixture mediators from the perspective of finite mixture distributions to flexibly capture such mediator data. Multiple data types are considered for modeling such mediators including the zero-inflated log-normal mixture, zero-inflated Poisson mixture and zero-inflated negative binomial mixture. A two-part mediation effect is derived to better understand effects on outcomes attributable to the numerical change as well as binary change from 0 to 1 in mediators. The maximum likelihood estimates are obtained by an expectation maximization algorithm to account for unobserved mixture membership and whether an observed zero is a true or false zero. The optimal number of mixture components are chosen by a model selection criterion. The performance of the proposed method is demonstrated in a simulation study and an application to a neuroscience study in comparison with standard mediation analysis methods.