A marginalized two-part Beta regression model for microbiome compositional data

摘要

Author summary Semi-continuous compositional data are typically analyzed using two-part models which separately describe the probability of zero values and the distribution of positive values. The second part of the model provides a conditional interpretation of covariate effects on the positive response. However, it is of great interest in many applications to assess the covariate effect on the marginal mean of the response. For this purpose, we propose a marginalized two-part model by reparameterizing the marginal mean in Part II. We show that the proposed marginalized two-part model outperforms conventional methods by simulation studies in terms of controlling the Type I error and maximizing the power. We apply our method to a microbiota dataset, and find consistent results with our simulation studies.