IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0233542.html
   My bibliography  Save this article

Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling

Author

Listed:
  • Patricia Gilholm
  • Kerrie Mengersen
  • Helen Thompson

Abstract

Identifying children who are at-risk for developmental delay, so that these children can have access to interventions as early as possible, is an important and challenging problem in developmental research. This research aimed to identify latent subgroups of children with developmental delay, by modelling and clustering developmental milestones. The main objectives were to (a) create a developmental profile for each child by modelling milestone achievements, from birth to three years of age, across multiple domains of development, and (b) cluster the profiles to identify groups of children who show similar deviations from typical development. The ensemble methodology used in this research consisted of three components: (1) Bayesian sequential updating was used to model the achievement of milestones, which allows for updated predictions of development to be made in real time; (2) a measure was created that indicated how far away each child deviated from typical development for each functional domain, by calculating the area between each child’s obtained sequence of posterior means and a sequence of posterior means representing typical development; and (3) Dirichlet process mixture modelling was used to cluster the obtained areas. The data used were 348 binary developmental milestone measurements, collected from birth to three years of age, from a small community sample of young children (N = 79). The model identified nine latent groups of children with similar features, ranging from no delays in all functional domains, to large delays in all domains. The performance of the Dirichlet process mixture model was validated with two simulation studies.

Suggested Citation

  • Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.
    • Handle: RePEc:plo:pone00:0233542
    DOI: 10.1371/journal.pone.0233542
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233542
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0233542&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0233542?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
    ---><---

    References listed on IDEAS

    as
    1. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
    2. Guosheng Yin & Nan Chen & J. Jack Lee, 2012. "Phase II trial design with Bayesian adaptive randomization and predictive probability," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 219-235, March.
    3. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    4. Kezi Yu & J Gerald Quirk & Petar M Djurić, 2017. "Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-16, September.
    5. Chunyan Cai & Ying Yuan & Yuan Ji, 2014. "A Bayesian dose finding design for oncology clinical trials of combinational biological agents," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 159-173, January.
    6. Nicole White & Helen Johnson & Peter Silburn & Kerrie Mengersen, 2012. "Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2363-2377, July.
    7. Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
    Full references (including those not matched with items on IDEAS)

    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. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    2. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
    3. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    4. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
    5. Sucharitha, Rahul Srinivas & Lee, Seokcheon, 2022. "GMM clustering for in-depth food accessibility pattern exploration and prediction model of food demand behavior," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    6. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
    7. Pati, Debdeep & Dunson, David B. & Tokdar, Surya T., 2013. "Posterior consistency in conditional distribution estimation," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 456-472.
    8. repec:cte:wsrepe:ws131211 is not listed on IDEAS
    9. Jingyi Zhang & Nolan A. Wages & Ruitao Lin, 2024. "SFU: Surface-Free Utility-Based Design for Dose Optimization in Cancer Drug Combination Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(3), pages 854-881, December.
    10. Konon, Alexander, 2016. "Career choice under uncertainty," VfS Annual Conference 2016 (Augsburg): Demographic Change 145583, Verein für Socialpolitik / German Economic Association.
    11. Roberto Mari & Roberto Rocci & Stefano Antonio Gattone, 2020. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 49-78, March.
    12. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
    13. Daniel W Kennedy & Jessica Cameron & Paul P -Y Wu & Kerrie Mengersen, 2021. "Peer groups for organisational learning: Clustering with practical constraints," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    14. Im, Yunju & Tan, Aixin, 2021. "Bayesian subgroup analysis in regression using mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    15. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    16. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    17. Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 45-61, March.
    18. Steve Su, 2016. "Flexible modelling of survival curves for censored data," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-20, December.
    19. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    20. Seo, Byungtae & Kim, Daeyoung, 2012. "Root selection in normal mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2454-2470.
    21. Rijan Shrestha & Tomasz Kozlowski, 2016. "Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation--maximization under Bayesian framework," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(6), pages 1011-1026, May.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0233542. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.