IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v11y2015i2p273-284n6.html
   My bibliography  Save this article

A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups

Author

Listed:
  • Das Kiranmoy

    (Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India)

  • Afriyie Prince
  • Spirko Lauren

    (Department of Statistics, Temple University, Philadelphia PA, 1912)

Abstract

Often the biological and/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.

Suggested Citation

  • Das Kiranmoy & Afriyie Prince & Spirko Lauren, 2015. "A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 273-284, November.
    • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:273-284:n:6
    DOI: 10.1515/ijb-2015-0002
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2015-0002
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2015-0002?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. Dunson, David B. & Xue, Ya & Carin, Lawrence, 2008. "The Matrix Stick-Breaking Process: Flexible Bayes Meta-Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 317-327, March.
    2. Yisheng Li & Xihong Lin & Peter Müller, 2010. "Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 66(1), pages 70-78, March.
    3. Jianxin Pan, 2003. "On modelling mean-covariance structures in longitudinal studies," Biometrika, Biometrika Trust, vol. 90(1), pages 239-244, March.
    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. Kiranmoy Das & Michael J. Daniels, 2014. "A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data," Biometrics, The International Biometric Society, vol. 70(1), pages 33-43, March.
    2. Xueying Zheng & Wing Fung & Zhongyi Zhu, 2013. "Robust estimation in joint mean–covariance regression model for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 617-638, August.
    3. 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.
    4. Kohli, Priya & Garcia, Tanya P. & Pourahmadi, Mohsen, 2016. "Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 87-100.
    5. Kim, Chulmin & Zimmerman, Dale L., 2012. "Unconstrained models for the covariance structure of multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 104-118.
    6. Dengke Xu & Zhongzhan Zhang & Liucang Wu, 2014. "Bayesian analysis of joint mean and covariance models for longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2504-2514, November.
    7. Rabe Anasu & Shangodoyin D. K. & Thaga K., 2019. "Linear Cholesky Decomposition Of Covariance Matrices In Mixed Models With Correlated Random Effects," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 59-70, December.
    8. Chen, Xue-Dong & Tang, Nian-Sheng, 2010. "Bayesian analysis of semiparametric reproductive dispersion mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2145-2158, September.
    9. Li, Erning & Pourahmadi, Mohsen, 2013. "An alternative REML estimation of covariance matrices in linear mixed models," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1071-1077.
    10. Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
    11. Bassetti, Federico & Casarin, Roberto & Leisen, Fabrizio, 2014. "Beta-product dependent Pitman–Yor processes for Bayesian inference," Journal of Econometrics, Elsevier, vol. 180(1), pages 49-72.
    12. Fei Wang & Lu Wang & Peter X.‐K. Song, 2016. "Fused lasso with the adaptation of parameter ordering in combining multiple studies with repeated measurements," Biometrics, The International Biometric Society, vol. 72(4), pages 1184-1193, December.
    13. Anasu Rabe & D. K. Shangodoyin & K. Thaga, 2019. "Linear Cholesky Decomposition Of Covariance Matrices In Mixed Models With Correlated Random Effects," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 59-70, December.
    14. Reyhaneh Rikhtehgaran & Iraj Kazemi, 2013. "Semi-parametric Bayesian estimation of mixed-effects models using the multivariate skew-normal distribution," Computational Statistics, Springer, vol. 28(5), pages 2007-2027, October.
    15. Zhang, Jianjun & Qiu, Chunjuan & Wu, Xianyi, 2018. "Bayesian ratemaking with common effects modeled by mixture of Polya tree processes," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 87-94.
    16. Lan Wang & Annie Qu, 2009. "Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 177-190, January.
    17. Guney, Yesim & Arslan, Olcay & Yavuz, Fulya Gokalp, 2022. "Robust estimation in multivariate heteroscedastic regression models with autoregressive covariance structures using EM algorithm," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    18. Sudhir Voleti & Praveen K. Kopalle & Pulak Ghosh, 2015. "An Interproduct Competition Model Incorporating Branding Hierarchy and Product Similarities Using Store-Level Data," Management Science, INFORMS, vol. 61(11), pages 2720-2738, November.
    19. Lee, Keunbaik & Lee, Chang-Hoon & Kwak, Min-Sun & Jang, Eun Jin, 2021. "Analysis of multivariate longitudinal data using ARMA Cholesky and hypersphere decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    20. Se Yoon Lee & Bani K. Mallick, 2022. "Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 1-43, 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:bpj:ijbist:v:11:y:2015:i:2:p:273-284:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.