IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i1p446-456.html
   My bibliography  Save this article

Inference in HIV dynamics models via hierarchical likelihood

Author

Listed:
  • Commenges, D.
  • Jolly, D.
  • Drylewicz, J.
  • Putter, H.
  • Thiébaut, R.

Abstract

HIV dynamical models are often based on non-linear systems of ordinary differential equations (ODE), which do not have an analytical solution. Introducing random effects in such models leads to very challenging non-linear mixed-effects models. To avoid the numerical computation of multiple integrals involved in the likelihood, a hierarchical likelihood (h-likelihood) approach, treated in the spirit of a penalized likelihood is proposed. The asymptotic distribution of the maximum h-likelihood estimators (MHLE) for fixed effects is given. The MHLE are slightly biased but the bias can be made negligible by using a parametric bootstrap procedure. An efficient algorithm for maximizing the h-likelihood is proposed. A simulation study, based on a classical HIV dynamical model, confirms the good properties of the MHLE. The method is applied to the analysis of a clinical trial.

Suggested Citation

  • Commenges, D. & Jolly, D. & Drylewicz, J. & Putter, H. & Thiébaut, R., 2011. "Inference in HIV dynamics models via hierarchical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 446-456, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:446-456
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00213-6
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Noh, Maengseok & Lee, Youngjo, 2008. "Hierarchical-likelihood approach for nonlinear mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3517-3527, March.
    2. J. Guedj & R. Thiébaut & D. Commenges, 2007. "Maximum Likelihood Estimation in Dynamical Models of HIV," Biometrics, The International Biometric Society, vol. 63(4), pages 1198-1206, December.
    3. Hongyu Miao & Carrie Dykes & Lisa M. Demeter & Hulin Wu, 2009. "Differential Equation Modeling of HIV Viral Fitness Experiments: Model Identification, Model Selection, and Multimodel Inference," Biometrics, The International Biometric Society, vol. 65(1), pages 292-300, 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. David D. Ho & Avidan U. Neumann & Alan S. Perelson & Wen Chen & John M. Leonard & Martin Markowitz, 1995. "Rapid Turnover of Plasma Virions and CD4 Lymphocytes in HIV-1 Infection," Working Papers 95-01-002, Santa Fe Institute.
    6. 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.
    7. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
    8. 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.
    9. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
    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. Qiu, Xing & Xu, Tao & Soltanalizadeh, Babak & Wu, Hulin, 2022. "Identifiability analysis of linear ordinary differential equation systems with a single trajectory," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    2. Iddi, Samuel & Molenberghs, Geert, 2012. "A combined overdispersed and marginalized multilevel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1944-1951.
    3. Tingting Yu & Lang Wu & Peter Gilbert, 2019. "New approaches for censored longitudinal data in joint modelling of longitudinal and survival data, with application to HIV vaccine studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 229-258, April.

    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. 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.
    2. Hanwen Huang, 2022. "Bayesian multi‐level mixed‐effects model for influenza dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1978-1995, November.
    3. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
    4. Hulin Wu & Hongqi Xue & Arun Kumar, 2012. "Numerical Discretization-Based Estimation Methods for Ordinary Differential Equation Models via Penalized Spline Smoothing with Applications in Biomedical Research," Biometrics, The International Biometric Society, vol. 68(2), pages 344-352, June.
    5. Dacheng Liu & Tao Lu & Xu-Feng Niu & Hulin Wu, 2011. "Mixed-Effects State-Space Models for Analysis of Longitudinal Dynamic Systems," Biometrics, The International Biometric Society, vol. 67(2), pages 476-485, June.
    6. 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.
    7. J. Guedj & R. Thiébaut & D. Commenges, 2007. "Maximum Likelihood Estimation in Dynamical Models of HIV," Biometrics, The International Biometric Society, vol. 63(4), pages 1198-1206, December.
    8. Jeremie Guedj & Rodolphe Thiébaut & Daniel Commenges, 2011. "Joint Modeling of the Clinical Progression and of the Biomarkers' Dynamics Using a Mechanistic Model," Biometrics, The International Biometric Society, vol. 67(1), pages 59-66, March.
    9. Mélanie Prague & Daniel Commenges & Julia Drylewicz & Rodolphe Thiébaut, 2012. "Treatment Monitoring of HIV-Infected Patients based on Mechanistic Models," Biometrics, The International Biometric Society, vol. 68(3), pages 902-911, September.
    10. 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.
    11. 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.
    12. Qianwen Tan & Subhashis Ghosal, 2021. "Bayesian Analysis of Mixed-effect Regression Models Driven by Ordinary Differential Equations," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 3-29, May.
    13. 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.
    14. 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.
    15. 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.
    16. González, Ramón E.R. & Coutinho, Sérgio & Zorzenon dos Santos, Rita Maria & de Figueirêdo, Pedro Hugo, 2013. "Dynamics of the HIV infection under antiretroviral therapy: A cellular automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4701-4716.
    17. Xinyu Zhang & Jiguo Cao & Raymond J. Carroll, 2017. "Estimating varying coefficients for partial differential equation models," Biometrics, The International Biometric Society, vol. 73(3), pages 949-959, September.
    18. Baisen Liu & Liangliang Wang & Yunlong Nie & Jiguo Cao, 2021. "Semiparametric Mixed-Effects Ordinary Differential Equation Models with Heavy-Tailed Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 428-445, September.
    19. Zhou, Jie & Han, Lu & Liu, Sanyang, 2013. "Nonlinear mixed-effects state space models with applications to HIV dynamics," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1448-1456.
    20. 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.

    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:55:y:2011:i:1:p:446-456. 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.