# CRAmed: a conditional randomization test for high-dimensional mediation analysis in sparse microbiome data

## TL;DR

CRAmed is a new statistical method for understanding how the microbiome influences health outcomes through mediation analysis.

## Contribution

CRAmed introduces a novel framework for mediation analysis that decomposes effects into presence-absence and abundance components in microbiome data.

## Key findings

- CRAmed outperformed existing methods in recall, precision, and F1 score in simulations.
- CRAmed demonstrated effectiveness and interpretability in real microbiome data applications.

## Abstract

Numerous microbiome studies have revealed significant associations between the microbiome and human health and disease. These findings have motivated researchers to explore the causal role of the microbiome in human complex traits and diseases. However, the complexities of microbiome data pose challenges for statistical analysis and interpretation of causal effects.

We introduced a novel statistical framework, CRAmed, for inferring the mediating role of the microbiome between treatment and outcome. CRAmed improved the interpretability of the mediation analysis by decomposing the natural indirect effect into two parts, corresponding to the presence–absence and abundance of a microbe, respectively. Comprehensive simulations demonstrated the superior performance of CRAmed in Recall, precision, and F1 score, with a notable level of robustness, compared to existing mediation analysis methods. Furthermore, two real data applications illustrated the effectiveness and interpretability of CRAmed. Our research revealed that CRAmed holds promise for uncovering the mediating role of the microbiome and understanding of the factors influencing host health.

The R package CRAmed implementing the proposed methods is available online at https://github.com/liudoubletian/CRAmed.