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A Mixture Model for Longitudinal Data with Application to Assessment of Noncompliance

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  • Donna K. Pauler
  • Nan M. Laird

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  • Donna K. Pauler & Nan M. Laird, 2000. "A Mixture Model for Longitudinal Data with Application to Assessment of Noncompliance," Biometrics, The International Biometric Society, vol. 56(2), pages 464-472, June.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:2:p:464-472
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.00464.x
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    Cited by:

    1. Wang, Xiaoning & Schumitzky, Alan & D'Argenio, David Z., 2009. "Population pharmacokinetic/pharmacodynamic mixture models via maximum a posteriori estimation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3907-3915, October.
    2. Casey Codd & Robert Cudeck, 2014. "Nonlinear Random-Effects Mixture Models for Repeated Measures," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 60-83, January.
    3. 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.
    4. Bartolucci, Al & Bae, Sejong & Singh, Karan & Griffith, H. Randall, 2009. "An examination of Bayesian statistical approaches to modeling change in cognitive decline in an Alzheimer's disease population," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(3), pages 561-571.
    5. Yangxin Huang & Xiaosun Lu & Jiaqing Chen & Juan Liang & Miriam Zangmeister, 2018. "Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 699-718, October.
    6. De la Cruz-Mesia, Rolando & Quintana, Fernando A. & Marshall, Guillermo, 2008. "Model-based clustering for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1441-1457, January.
    7. Shuang Zhang & Xingdong Feng, 2022. "Distributed identification of heterogeneous treatment effects," Computational Statistics, Springer, vol. 37(1), pages 57-89, March.
    8. Francisca Galindo Garre & Aeilko H. Zwinderman & Ronald B. Geskus & Yvo W. J. Sijpkens, 2008. "A joint latent class changepoint model to improve the prediction of time to graft failure," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 299-308, January.
    9. Shaikh Mateen & McNicholas Paul D & Desmond Anthony F, 2010. "A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-17, March.

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