IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v93y2016icp119-130.html
   My bibliography  Save this article

Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies

Author

Listed:
  • Lu, Xiaosun
  • Huang, Yangxin
  • Zhu, Yiliang

Abstract

It is a common practice to analyze longitudinal data using nonlinear mixed-effects (NLME) models. However, the following issues may standout. (i) Individuals may be possibly from a heterogeneous population following more than one mean trajectories, while a homogeneous population assumption for model structure may be unrealistically obscuring important features of between- and within-subject variations; (ii) some covariates may be missing and/or measured with errors. There has been few studies concerning both population heterogeneity and covariates measured with errors and missing data features simultaneously in longitudinal data analysis. A finite mixture of NLME joint (FMNLMEJ) models is developed to address simultaneous impact of both features under Bayesian framework, which offers a route to estimate not only model parameters but also probabilities of class membership. An AIDS data set is analyzed to demonstrate the methodologies in comparison of the proposed FMNLMEJ model with a commonly used NLME model.

Suggested Citation

  • Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
    • Handle: RePEc:eee:csdana:v:93:y:2016:i:c:p:119-130
    DOI: 10.1016/j.csda.2014.04.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947314000991
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.04.003?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. Yangxin Huang & Dacheng Liu & Hulin Wu, 2006. "Hierarchical Bayesian Methods for Estimation of Parameters in a Longitudinal HIV Dynamic System," Biometrics, The International Biometric Society, vol. 62(2), pages 413-423, June.
    2. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    3. Donna K. Pauler & Nan M. Laird, 2000. "A Mixture Model for Longitudinal Data with Application to Assessment of Noncompliance," Biometrics, The International Biometric Society, vol. 56(2), pages 464-472, June.
    4. Wei Liu & Lang Wu, 2007. "Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses," Biometrics, The International Biometric Society, vol. 63(2), pages 342-350, June.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    7. Mian Huang & Weixin Yao, 2012. "Mixture of Regression Models With Varying Mixing Proportions: A Semiparametric Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 711-724, June.
    8. Yao, Weixin & Lindsay, Bruce G., 2009. "Bayesian Mixture Labeling by Highest Posterior Density," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 758-767.
    9. Alan S. Perelson & Paulina Essunger & Yunzhen Cao & Mika Vesanen & Arlene Hurley & Kalle Saksela & Martin Markowitz & David D. Ho, 1997. "Decay characteristics of HIV-1-infected compartments during combination therapy," Nature, Nature, vol. 387(6629), pages 188-191, May.
    10. Hulin Wu & A. Adam Ding, 1999. "Population HIV-1 Dynamics In Vivo: Applicable Models and Inferential Tools for Virological Data from AIDS Clinical Trials," Biometrics, The International Biometric Society, vol. 55(2), pages 410-418, June.
    11. Yangxin Huang & Getachew Dagne, 2011. "A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates," Biometrics, The International Biometric Society, vol. 67(1), pages 260-269, March.
    12. Wu L., 2002. "A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error, With Application to AIDS Studies," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 955-964, December.
    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. Xiaosun Lu & Yangxin Huang & Rong Zhou, 2016. "Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2713-2728, November.
    2. Alberto Bucci & Lorenzo Carbonari & Monia Ranalli & Giovanni Trovato, 2019. "Health and Development," CEIS Research Paper 470, Tor Vergata University, CEIS, revised 24 Mar 2021.

    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. Hanze Zhang & Yangxin Huang, 2020. "Quantile regression-based Bayesian joint modeling analysis of longitudinal–survival data, with application to an AIDS cohort study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 339-368, April.
    2. Dagne Getachew & Huang Yangxin, 2012. "Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-24, September.
    3. Yangxin Huang & Getachew Dagne, 2011. "A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates," Biometrics, The International Biometric Society, vol. 67(1), pages 260-269, March.
    4. Huang, Yangxin, 2008. "Long-term HIV dynamic models incorporating drug adherence and resistance to treatment for prediction of virological responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3765-3778, March.
    5. Wan-Lun Wang & Yu-Chen Yang & Tsung-I Lin, 2024. "Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights," 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. 18(2), pages 271-307, June.
    6. Yangxin Huang & X. Hu & Getachew Dagne, 2014. "Jointly modeling time-to-event and longitudinal data: a Bayesian approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 95-121, March.
    7. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    8. Xue, Jiacheng & Yao, Weixin, 2022. "Machine Learning Embedded Semiparametric Mixtures of Regressions with Covariate-Varying Mixing Proportions," Econometrics and Statistics, Elsevier, vol. 22(C), pages 159-171.
    9. Huang Yangxin & Chen Jiaqing & Yan Chunning, 2012. "Mixed-Effects Joint Models with Skew-Normal Distribution for HIV Dynamic Response with Missing and Mismeasured Time-Varying Covariate," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-30, November.
    10. Yangxin Huang & Tao Lu, 2017. "Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features," Computational Statistics, Springer, vol. 32(1), pages 179-196, March.
    11. Sijia Xiang & Weixin Yao, 2020. "Semiparametric mixtures of regressions with single-index for model based clustering," 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. 14(2), pages 261-292, June.
    12. Jianwei Chen, 2010. "Modelling long‐term human immunodeficiency virus dynamic models with application to acquired immune deficiency syndrome clinical study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 805-820, November.
    13. Yangxin Huang & Getachew Dagne, 2012. "Bayesian Semiparametric Nonlinear Mixed-Effects Joint Models for Data with Skewness, Missing Responses, and Measurement Errors in Covariates," Biometrics, The International Biometric Society, vol. 68(3), pages 943-953, September.
    14. Sijia Xiang & Weixin Yao, 2018. "Semiparametric mixtures of nonparametric regressions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 131-154, February.
    15. Xiaosun Lu & Yangxin Huang & Rong Zhou, 2016. "Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2713-2728, November.
    16. Liu, Baisen & Wang, Liangliang & Nie, Yunlong & Cao, Jiguo, 2019. "Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 233-246.
    17. Christian E. Galarza & Luis M. Castro & Francisco Louzada & Victor H. Lachos, 2020. "Quantile regression for nonlinear mixed effects models: a likelihood based perspective," Statistical Papers, Springer, vol. 61(3), pages 1281-1307, June.
    18. L. Wu & W. Liu & X. J. Hu, 2010. "Joint Inference on HIV Viral Dynamics and Immune Suppression in Presence of Measurement Errors," Biometrics, The International Biometric Society, vol. 66(2), pages 327-335, June.
    19. Ye, Mao & Lu, Zhao-Hua & Li, Yimei & Song, Xinyuan, 2019. "Finite mixture of varying coefficient model: Estimation and component selection," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 452-474.
    20. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.

    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:eee:csdana:v:93:y:2016:i:c:p:119-130. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.