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

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  • 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
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    References listed on IDEAS

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    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. 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.
    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. 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.
    5. 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.
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    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.

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