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Modeling Markers of Disease Progression by a Hidden Markov Process: Application to Characterizing CD4 Cell Decline

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  • Chantal Guihenneuc-Jouyaux
  • Sylvia Richardson
  • Ira M. Longini Jr.

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  • Chantal Guihenneuc-Jouyaux & Sylvia Richardson & Ira M. Longini Jr., 2000. "Modeling Markers of Disease Progression by a Hidden Markov Process: Application to Characterizing CD4 Cell Decline," Biometrics, The International Biometric Society, vol. 56(3), pages 733-741, September.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:3:p:733-741
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.00733.x
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    References listed on IDEAS

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    1. Niels Keiding, 1991. "Age‐Specific Incidence and Prevalence: A Statistical Perspective," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(3), pages 371-396, May.
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    Cited by:

    1. Bo Henry Lindqvist, 2023. "Phase-type models for competing risks, with emphasis on identifiability issues," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 318-341, April.
    2. Chris Sherlock & Tatiana Xifara & Sandra Telfer & Mike Begon, 2013. "A coupled hidden Markov model for disease interactions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 609-627, August.
    3. Marc Chadeau‐Hyam & Paul S. Clarke & Chantal Guihenneuc‐Jouyaux & Simon N. Cousens & Robert G. Will & Azra C. Ghani, 2010. "An application of hidden Markov models to the French variant Creutzfeldt–Jakob disease epidemic," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 839-853, November.
    4. R. A. Hubbard & L. Y. T. Inoue & J. R. Fann, 2008. "Modeling Nonhomogeneous Markov Processes via Time Transformation," Biometrics, The International Biometric Society, vol. 64(3), pages 843-850, September.
    5. Ting Wang & Jiancang Zhuang & Kazushige Obara & Hiroshi Tsuruoka, 2017. "Hidden Markov modelling of sparse time series from non-volcanic tremor observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 691-715, August.
    6. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    7. Jiakun Jiang & Wei Yang & Erin M. Schnellinger & Stephen E. Kimmel & Wensheng Guo, 2023. "Dynamic logistic state space prediction model for clinical decision making," Biometrics, The International Biometric Society, vol. 79(1), pages 73-85, March.
    8. Spagnoli, Alessandra & Henderson, Robin & Boys, Richard J. & Houwing-Duistermaat, Jeanine J., 2011. "A hidden Markov model for informative dropout in longitudinal response data with crisis states," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 730-738, July.
    9. Albrecher, Hansjörg & Bladt, Martin & Bladt, Mogens & Yslas, Jorge, 2022. "Mortality modeling and regression with matrix distributions," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 68-87.

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