IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v13y2021i2d10.1007_s12561-020-09295-y.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-020-09295-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-020-09295-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vincent Goulet & Christophe Dutang & Mathieu Pigeon, 2008. "actuar : An R Package for Actuarial Science," Post-Print hal-01616144, HAL.
    2. 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.
    3. George M. Weinstock, 2012. "Genomic approaches to studying the human microbiota," Nature, Nature, vol. 489(7415), pages 250-256, September.
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:jss:jstsof:35:i10 is not listed on IDEAS
    2. Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    3. Anna Castañer & M.Mercè Claramunt & Maite Mármol, 2014. "Some optimization and decision problems in proportional reinsurance," UB School of Economics Working Papers 2014/310, University of Barcelona School of Economics.
    4. K. G. Reddy & M. G. M. Khan, 2020. "stratifyR: An R Package for optimal stratification and sample allocation for univariate populations," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 383-405, September.
    5. Denuit, Michel, 2019. "Size-biased transform and conditional mean risk sharing, with application to P2P insurance and tontines," LIDAM Discussion Papers ISBA 2019010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Michael Grabchak, 2022. "Discrete Tempered Stable Distributions," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1877-1890, September.
    7. Denuit, Michel, 2019. "Investing in your own and peers' risks: The simple analytics of p2p insurance," LIDAM Discussion Papers ISBA 2019028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Ekaterina Bulinskaya & Boris Shigida, 2021. "Discrete-Time Model of Company Capital Dynamics with Investment of a Certain Part of Surplus in a Non-Risky Asset for a Fixed Period," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 103-121, March.
    9. Adele H. Marshall & Mariangela Zenga, 2012. "Experimenting with the Coxian Phase-Type Distribution to Uncover Suitable Fits," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 71-86, March.
    10. Ozkok, Erengul & Streftaris, George & Waters, Howard R. & Wilkie, A. David, 2012. "Bayesian modelling of the time delay between diagnosis and settlement for Critical Illness Insurance using a Burr generalised-linear-type model," Insurance: Mathematics and Economics, Elsevier, vol. 50(2), pages 266-279.
    11. Abu Bakar, S.A. & Hamzah, N.A. & Maghsoudi, M. & Nadarajah, S., 2015. "Modeling loss data using composite models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 146-154.
    12. Hassan Mazengera, 2017. "Revenue-based lending for SMEs," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 1-20, June.
    13. Emanuele Taufer & Flavio Santi & Pier Luigi Novi Inverardi & Giuseppe Espa & Maria Michela Dickson, 2020. "Extreme Value Index Estimation by Means of an Inequality Curve," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    14. Kobus, Paweł, 2013. "Modelling joint distribution of crop plant yields and prices with use of a copula function," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 13(28), pages 1-10, December.
    15. Alexandru Amărioarei & Frankie Spencer & Gefry Barad & Ana-Maria Gheorghe & Corina Iţcuş & Iris Tuşa & Ana-Maria Prelipcean & Andrei Păun & Mihaela Păun & Alfonso Rodriguez-Paton & Romică Trandafir & , 2021. "DNA-Guided Assembly for Fibril Proteins," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    16. Jelena Kočović & Marija Koprivica, 2018. "Internal Model For Measuring Premium Risk In Determining Solvency Of Non-Life Insurers," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 63(217), pages 99-128, April – J.
    17. Devolder, Pierre, 2019. "Une alternative a la pension a points : le compte individuel pension en euros," LIDAM Discussion Papers ISBA 2019011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Tugba Akkaya Hocagil & Richard J. Cook & Sandra W. Jacobson & Joseph L. Jacobson & Louise M. Ryan, 2021. "Propensity score analysis for a semi‐continuous exposure variable: a study of gestational alcohol exposure and childhood cognition," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1390-1413, October.
    19. Keita, Moussa, 2017. "Traitement et Analyse de Données avec le Langage R [Data Processing and Analysis with R Language]," MPRA Paper 77718, University Library of Munich, Germany.
    20. Michel Denuit & Christian Y. Robert, 2024. "Conditional Mean Risk Sharing of Independent Discrete Losses in Large Pools," Methodology and Computing in Applied Probability, Springer, vol. 26(4), pages 1-22, December.
    21. Kerstin Thriene & Karin B. Michels, 2023. "Human Gut Microbiota Plasticity throughout the Life Course," IJERPH, MDPI, vol. 20(2), pages 1-14, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:13:y:2021:i:2:d:10.1007_s12561-020-09295-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.