IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v21y2016i2d10.1007_s13253-015-0242-1.html
   My bibliography  Save this article

A Nonlinear Mixed-Effects Model for Multivariate Longitudinal Data with Dropout with Application to HIV Disease Dynamics

Author

Listed:
  • Artz G. Luwanda

    (University of KwaZulu-Natal)

  • Henry G. Mwambi

    (University of KwaZulu-Natal)

Abstract

The main challenge in biomedical and clinical studies which involve collection of longitudinal data is the premature withdrawal of the subjects from the study resulting in incomplete data. Standard statistical analysis approaches usually give biased estimates of the model parameters if the mechanisms that led to dropout are ignored. In this discussion, we consider nonlinear mixed-effects models for multivariate longitudinal data in the presence of subject dropout. We present techniques for estimation of model parameters. These procedures are applied to estimate the parameters in the HIV dynamic system using routine observational data from an HIV clinic.

Suggested Citation

  • Artz G. Luwanda & Henry G. Mwambi, 2016. "A Nonlinear Mixed-Effects Model for Multivariate Longitudinal Data with Dropout with Application to HIV Disease Dynamics," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 277-294, June.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:2:d:10.1007_s13253-015-0242-1
    DOI: 10.1007/s13253-015-0242-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-015-0242-1
    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/s13253-015-0242-1?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. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    2. 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.
    3. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Debajyoti Sinha & Michael Parzen & Steven Lipshultz, 2009. "Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 3-20, January.
    4. Roy J. & Lin X., 2002. "Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates: Changes in Methadone Treatment Practices," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 40-52, March.
    5. Bo Cai & David B. Dunson & Joseph B. Stanford, 2010. "Dynamic Model for Multivariate Markers of Fecundability," Biometrics, The International Biometric Society, vol. 66(3), pages 905-913, September.
    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. Vanderly Janeiro & Robson Marcelo Rossi & Terezinha Aparecida Guedes & Ana Beatriz Tozzo Martins & Lucimary Afonso dos Santos, 2022. "Nonlinear Mixed Models Applied to Ruminal Degradability Studies," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(5), pages 1-18, November.

    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. David M. Murray & Jonathan L. Blitstein, 2003. "Methods To Reduce The Impact Of Intraclass Correlation In Group-Randomized Trials," Evaluation Review, , vol. 27(1), pages 79-103, February.
    2. Patrick E. B. FitzGerald, 2002. "Extended Generalized Estimating Equations for Binary Familial Data with Incomplete Families," Biometrics, The International Biometric Society, vol. 58(4), pages 718-726, December.
    3. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    4. E. Michael Foster & Grace Y. Fang, 2004. "Alternative Methods for Handling Attrition," Evaluation Review, , vol. 28(5), pages 434-464, October.
    5. Mette Ejrnæs & Anders Holm, 2006. "Comparing Fixed Effects and Covariance Structure Estimators for Panel Data," Sociological Methods & Research, , vol. 35(1), pages 61-83, August.
    6. Valdemar Melicher & Tom Haber & Wim Vanroose, 2017. "Fast derivatives of likelihood functionals for ODE based models using adjoint-state method," Computational Statistics, Springer, vol. 32(4), pages 1621-1643, December.
    7. Miran A. Jaffa & Ayad A. Jaffa, 2019. "A Likelihood-Based Approach with Shared Latent Random Parameters for the Longitudinal Binary and Informative Censoring Processes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 597-613, December.
    8. Shu Xu & Shelley A. Blozis, 2011. "Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 237-256, April.
    9. Bian, Yuan & Yi, Grace Y. & He, Wenqing, 2024. "A unified framework of analyzing missing data and variable selection using regularized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    10. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
    11. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2013. "Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model," Ecological Modelling, Elsevier, vol. 263(C), pages 56-63.
    12. Congran Yu & Wenwen Guo & Xinyuan Song & Hengjian Cui, 2023. "Feature screening with latent responses," Biometrics, The International Biometric Society, vol. 79(2), pages 878-890, June.
    13. Lars Relund Nielsen & Erik Jørgensen & Søren Højsgaard, 2011. "Embedding a state space model into a Markov decision process," Annals of Operations Research, Springer, vol. 190(1), pages 289-309, October.
    14. Jennifer Chan & Wai Wan, 2011. "Bayesian approach to analysing longitudinal bivariate binary data with informative dropout," Computational Statistics, Springer, vol. 26(1), pages 121-144, March.
    15. Jayajit Chakraborty & Timothy W. Collins & Sara E. Grineski & Alejandra Maldonado, 2017. "Racial Differences in Perceptions of Air Pollution Health Risk: Does Environmental Exposure Matter?," IJERPH, MDPI, vol. 14(2), pages 1-16, January.
    16. Yang, Ying & Kang, Jian, 2010. "Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 193-207, January.
    17. Zhixiong Zhou & Shanshan Dong & Jun Yin & Quan Fu & Hong Ren & Zenong Yin, 2018. "Improving Physical Fitness and Cognitive Functions in Middle School Students: Study Protocol for the Chinese Childhood Health, Activity and Motor Performance Study (Chinese CHAMPS)," IJERPH, MDPI, vol. 15(5), pages 1-15, May.
    18. Sotto, Cristina & Beunckens, Caroline & Molenberghs, Geert & Kenward, Michael G., 2011. "MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 301-311, January.
    19. Yu Cao & Nitai D. Mukhopadhyay, 2021. "Statistical Modeling of Longitudinal Data with Non-Ignorable Non-Monotone Missingness with Semiparametric Bayesian and Machine Learning Components," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 152-169, May.
    20. Kenneth A. Bollen & Patrick J. Curran, 2004. "Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions," Sociological Methods & Research, , vol. 32(3), pages 336-383, February.

    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:spr:jagbes:v:21:y:2016:i:2:d:10.1007_s13253-015-0242-1. 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.