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An Approach to Analyze Longitudinal Zero-Inflated Microbiome Count Data Using Two-Stage Mixed Effects Models

Author

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  • Jian Wang

    (The University of Texas MD Anderson Cancer Center)

  • Cielito C. Reyes-Gibby

    (The University of Texas MD Anderson Cancer Center)

  • Sanjay Shete

    (The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

Abstract

The developments of powerful next-generation technologies provide valuable resources for investigating the human microbiome. In particular, it has been of great interest to study the longitudinal changes in the microbiome and its association with risk factors and clinical outcomes (e.g., longitudinal oral microbiome abundance and oral mucositis). The challenges of such analysis include the zero-inflated microbial abundance counts data and the correlation among the longitudinal abundance counts collected across different time points within the same patient. The current approaches for longitudinal zero-inflated microbiome abundance data focused on testing the covariate-taxon associations (i.e., time-varying abundance as the dependent variable), but ignored the taxon-outcome associations (i.e., non-time-varying clinical outcome as the dependent variable). To address this question, we proposed a two-stage mixed effects model for analyzing zero-inflated longitudinal data and the clinical outcome of interest. In this model, the longitudinal microbial abundance count data are first modeled as a function of time using the zero-inflated negative binomial mixed effects model, and at the second stage, the summaries of the temporal patterns (e.g., random intercepts and slopes) are used in the regression models (e.g., linear, accelerate failure time) to assess their associations with the outcome. Simulations showed that the two-stage mixed effects model can provide accurate estimations for the regression coefficients of the association between the longitudinal trend of microbial abundance and the outcome. We applied the proposed approach to the study of longitudinal patterns in oral microbial abundance and oral mucositis in the patients with squamous cell carcinoma of the head and neck.

Suggested Citation

  • Jian Wang & Cielito C. Reyes-Gibby & Sanjay Shete, 2021. "An Approach to Analyze Longitudinal Zero-Inflated Microbiome Count Data Using Two-Stage Mixed Effects Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 267-290, July.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:2:d:10.1007_s12561-020-09295-y
    DOI: 10.1007/s12561-020-09295-y
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    References listed on IDEAS

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    1. Haitao Chai & Hongmei Jiang & Lu Lin & Lei Liu, 2018. "A marginalized two-part Beta regression model for microbiome compositional data," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-16, July.
    2. George M. Weinstock, 2012. "Genomic approaches to studying the human microbiota," Nature, Nature, vol. 489(7415), pages 250-256, September.
    3. Vincent Goulet & Christophe Dutang & Mathieu Pigeon, 2008. "actuar : An R Package for Actuarial Science," Post-Print hal-01616144, HAL.
    4. Dutang, Christophe & Goulet, Vincent & Pigeon, Mathieu, 2008. "actuar: An R Package for Actuarial Science," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i07).
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    Cited by:

    1. Chung‐Wei Shen & Chun‐Shu Chen, 2024. "Estimation and selection for spatial zero‐inflated count models," Environmetrics, John Wiley & Sons, Ltd., vol. 35(4), June.

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