Exploring Causal Mediation Analysis in Bacterial Vaginosis Challenges

TL;DR

This paper reviews methods for causal mediation analysis in bacterial vaginosis research, emphasizing empirical approaches suitable for small samples to identify microbiome mediators linking behaviors to BV risk.

Contribution

It introduces and compares various causal mediation analysis methods tailored for microbiome data, highlighting the empirical distribution approach for small sample studies.

Findings

Empirical distribution method improves mediation analysis with small samples.

Models can identify specific bacterial taxa mediating behavior and BV risk.

Revisiting case studies demonstrates practical application of proposed methods.

Abstract

Bacterial Vaginosis (BV) affects nearly 23-29% of women worldwide and increases risk of miscarriage, preterm birth, and sexually transmitted infections. It involves a shift in the vaginal microbiome from Lactobacillus dominance to a diverse bacterial composition. Understanding causal pathways linking behavioral factors to BV risk is essential for effective intervention. Observational studies have identified pathogenic bacteria associated with BV, and causal mediation analysis can clarify how behaviors like sexual activity influence the microbiome. Analyzing microbiome data is complex due to its high-dimensional and compositional nature, often challenging traditional statistical methods, especially with small samples. This article presents various approaches to measure causal mediation effects, emphasizing the benefits of an empirical distribution method for small samples, and outlines models for mediators, exposure, and outcomes, aiming to identify taxa that mediate the exposure-outcome relationship in BV, concluding with a revisit of the motivational example and model identification.