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.

翻译：微生物组在疾病发病机制中的作用是一个新兴领域，有充分证据表明菌群失调与癌前及癌性状态相关。由于数据稀疏性、成分约束及潜在异质性，微生物组数据给因果中介分析带来了重大挑战。为解决这些问题，我们提出了一种零膨胀贝塔混合（ZIBM）方法，用于处理成分型微生物组中介变量的中介分析。该方法通过零膨胀分量处理过量零值，并利用贝塔混合分布捕捉非零相对丰度的异质性。在潜在结果框架内，ZIBM能够估计微生物组介导的边际因果效应，模型参数通过期望最大化算法进行计算。模拟研究表明，与现有方法相比，ZIBM在微生物组数据常见条件下能够提供更准确的估计和可靠的推断。一项针对真实微生物组研究的应用进一步证明了其实用价值。这些结果表明，所提方法为涉及成分型微生物组数据的中介分析提供了更灵活稳健的统计框架。