A Zero-Inflated Beta Mixture Model for Marginal Mediation Analysis with Compositional Microbiome Mediators

TL;DR

This paper introduces a zero-inflated beta mixture model to improve causal mediation analysis with compositional microbiome data, addressing data sparsity and heterogeneity.

Contribution

The paper proposes a novel ZIBM method that handles excess zeros and heterogeneity in microbiome data for more accurate mediation analysis.

Findings

ZIBM outperforms existing methods in simulation studies.

The method provides reliable estimates of microbiome-mediated causal effects.

Application to real data demonstrates practical utility.

Abstract

The role of the microbiome in disease pathogenesis is an emerging field with strong evidence suggesting that dysbiosis is associated with precancerous and cancerous states. Microbiome data present substantial challenges for causal mediation analysis due to sparsity, compositional constraints, and latent heterogeneity. To address these issues, we propose a zero-inflated beta mixture (ZIBM) method for mediation analysis with compositional microbiome mediators. The proposed method accommodates excess zeros through a zero-inflation component and captures heterogeneity in non-zero relative abundances using a beta mixture distribution. Within the potential-outcomes framework, the ZIBM provides estimates of marginal microbiome-mediated causal effects, and model parameters are estimated using an expectation-maximization algorithm. Simulation studies demonstrate that the ZIBM yields more accurate estimation and reliable inference under conditions commonly observed in microbiome data, compared with existing approaches. An application to a real microbiome study further illustrates its practical utility. These results indicate that the proposed method provides a more flexible and robust statistical framework for mediation analysis involving compositional microbiome data.