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Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model

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  • Samson, Adeline
  • Lavielle, Marc
  • Mentre, France

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  • Samson, Adeline & Lavielle, Marc & Mentre, France, 2006. "Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1562-1574, December.
    • Handle: RePEc:eee:csdana:v:51:y:2006:i:3:p:1562-1574
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    References listed on IDEAS

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    1. James P. Hughes, 1999. "Mixed Effects Models with Censored Data with Application to HIV RNA Levels," Biometrics, The International Biometric Society, vol. 55(2), pages 625-629, June.
    2. Lang Wu, 2004. "Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 700-709, January.
    3. A. Adam Ding & Hulin Wu, 2000. "A Comparison Study of Models and Fitting Procedures for Biphasic Viral Dynamics in HIV-1 Infected Patients Treated with Antiviral Therapies," Biometrics, The International Biometric Society, vol. 56(1), pages 293-300, March.
    4. 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.
    5. 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.
    6. 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.
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    Cited by:

    1. Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Larissa A. Matos & Víctor H. Lachos & Tsung-I Lin & Luis M. Castro, 2019. "Heavy-tailed longitudinal regression models for censored data: a robust parametric approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 844-878, September.
    3. Tao Lu & Yangxin Huang & Min Wang & Feng Qian, 2014. "A refined parameter estimating approach for HIV dynamic model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1645-1657, August.
    4. 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.
    5. Glen, Andrew G., 2010. "Accurate estimation with one order statistic," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1434-1441, June.
    6. Hongbin Zhang, 2023. "Stochastic EM Algorithm for Joint Model of Logistic Regression and Mechanistic Nonlinear Model in Longitudinal Studies," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
    7. 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.
    8. Larissa A. Matos & Luis M. Castro & Víctor H. Lachos, 2016. "Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 627-653, December.

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