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A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times

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  • Jason Roy
  • Michael J. Daniels

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  • Jason Roy & Michael J. Daniels, 2008. "A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times," Biometrics, The International Biometric Society, vol. 64(2), pages 538-545, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:538-545
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00884.x
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    References listed on IDEAS

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    1. Jiang Lin & Daowen Zhang & Marie Davidian, 2006. "Smoothing Spline-Based Score Tests for Proportional Hazards Models," Biometrics, The International Biometric Society, vol. 62(3), pages 803-812, September.
    2. Zengri Wang, 2003. "Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function," Biometrika, Biometrika Trust, vol. 90(4), pages 765-775, December.
    3. Patrick J. Heagerty, 2002. "Marginalized Transition Models and Likelihood Inference for Longitudinal Categorical Data," Biometrics, The International Biometric Society, vol. 58(2), pages 342-351, June.
    4. Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, September.
    5. Jason Roy, 2003. "Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model," Biometrics, The International Biometric Society, vol. 59(4), pages 829-836, December.
    6. Cook, J.A. & Grey, D. & Burke, J. & Cohen, M.H. & Gurtman, A.C. & Richardson, J.L. & Wilson, T.E. & Young, M.A. & Hessol, N.A., 2004. "Depressive symptoms and AIDS-related mortality among a multisite cohort of HIV-positive women," American Journal of Public Health, American Public Health Association, vol. 94(7), pages 1133-1140.
    7. Haiqun Lin & Charles E. McCulloch & Robert A. Rosenheck, 2004. "Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 60(2), pages 295-305, June.
    8. Kenneth J. Wilkins & Garrett M. Fitzmaurice, 2006. "A Hybrid Model for Nonignorable Dropout in Longitudinal Binary Responses," Biometrics, The International Biometric Society, vol. 62(1), pages 168-176, March.
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    Citations

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    Cited by:

    1. 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.
    2. Maria Marino & Marco Alfó, 2015. "Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 483-502, December.
    3. Jaeil Ahn & Suyu Liu & Wenyi Wang & Ying Yuan, 2013. "Bayesian Latent-Class Mixed-Effect Hybrid Models for Dyadic Longitudinal Data with Non-Ignorable Dropouts," Biometrics, The International Biometric Society, vol. 69(4), pages 914-924, December.
    4. Michael J. Daniels & Minji Lee & Wei Feng, 2023. "Dirichlet process mixture models for the analysis of repeated attempt designs," Biometrics, The International Biometric Society, vol. 79(4), pages 3907-3915, December.
    5. 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.
    6. M.J. Daniels & C. Wang & B.H. Marcus, 2014. "Fully Bayesian inference under ignorable missingness in the presence of auxiliary covariates," Biometrics, The International Biometric Society, vol. 70(1), pages 62-72, March.

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