A Bayesian Functional Concurrent Zero-Inflated Dirichlet-Multinomial Regression Model with Application to Infant Microbiome
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贝叶斯函数型同期零膨胀狄利克雷-多项回归模型及其在婴儿微生物组中的应用
- 研究问题
- 摘要称，研究旨在刻画暴露或处理与婴儿微生物组组成之间随时间变化的关联，并同时应对重复测量、过度离散、成分性、高维参数空间与零膨胀等建模难题。
- 方法与思路
- 作者提出函数型同期零膨胀狄利克雷-多项（FunC-ZIDM）回归模型，在狄利克雷-多项框架内同时处理零膨胀、成分结构与重复测量，以估计协变量与菌群分类单元之间随时间变化的函数型关系，并用模拟评估估计与扩展性。
- 材料陈述的贡献
- 摘要称，该模型填补了现有狄利克雷-多项类回归模型无法处理重复测量设计的空白；模拟显示其能较准确估计潜在函数关系并可扩展至较大成分空间；应用于出生后11周婴儿微生物组数据，发现α多样性与较高胎龄及膳食中母乳比例正相关。
- 阅读关注点
- 应核对模型的具体识别与先验设定、函数型效应的基函数或平滑方式、零膨胀部分的建模结构、模拟设计与其覆盖情形，以及11周数据中协变量调整、样本量与关联是否被解释为因果。
- 阅读边界
- 仅依据arXiv预印本摘要，未阅读全文；摘要未说明识别假设、估计细节、模拟规模与真实数据结果的不确定性，且预印本未经同行评审，不能据此声称已被期刊接收或结论已获验证。
原文摘要
The infant microbiome undergoes rapid changes in composition over time and is associated with long-term risks of conditions such as immune strength, allergy, asthma, and other health outcomes. Modeling the associations between exposures or treatments and microbial composition over time is essential for understanding the factors that drive these changes. Estimating these temporal dynamics has several challenges including: repeated measures, overdispersion, compositionality, high-dimensional parameter spaces, and zero-inflation. Many longitudinal regression models used in human microbiome research assume constant effects over time that cannot capture time-varying or functional effects of exposures, ignore the compositional structure of the data by modeling each taxon separately, and are not equipped to handle potential zero-inflation. Dirichlet-multinomial (DM) regression models inherently accommodate overdispersion and the compositional structure of the data and have been extended to account for excess zeros. However, existing DM-based regression models are unable to additionally handle repeated measures designs. To fill this gap, we propose a functional concurrent zero-inflated Dirichlet-multinomial (FunC-ZIDM) regression model which is designed to model time-varying relations between observed covariates and microbial taxa while accounting for zero-inflation, compositionality, and repeated measures. Through simulation, we demonstrate that the model can accurately estimate the underlying functional relations and scale to large compositional spaces. We apply our model to investigate time-varying associations between infant microbiome composition and observed covariates during the 11-week postnatal period. We found that $α$-diversity (i.e., diversity of the microbiome within an individual) is positively associated with a higher gestational age and percentage of breast milk in the diet.