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A Transitional Model for Longitudinal Binary Data Subject to Nonignorable Missing Data

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  • Paul S. Albert

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  • Paul S. Albert, 2000. "A Transitional Model for Longitudinal Binary Data Subject to Nonignorable Missing Data," Biometrics, The International Biometric Society, vol. 56(2), pages 602-608, June.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:2:p:602-608
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.00602.x
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    References listed on IDEAS

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    1. Mark R. Conaway, 1993. "Non‐Ignorable Non‐Response Models for Time‐Ordered Categorical Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 105-115, March.
    2. Isabelle Deltour & Sylvia Richardson & Jean-Yves Le Hesran, 1999. "Stochastic Algorithms for Markov Models Estimation with Intermittent Missing Data," Biometrics, The International Biometric Society, vol. 55(2), pages 565-573, June.
    3. X. Liu & C. Waternaux & E. Petkova, 1999. "Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop‐out," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(1), pages 103-115.
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    Cited by:

    1. Francesco Bartolucci & Alessio Farcomeni, 2015. "A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates," Biometrics, The International Biometric Society, vol. 71(1), pages 80-89, March.
    2. Eugenia Buta & Stephanie S. O’Malley & Ralitza Gueorguieva, 2018. "Bayesian joint modelling of longitudinal data on abstinence, frequency and intensity of drinking in alcoholism trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 869-888, June.
    3. Wei Liu & Bo Zhang & Zhiwei Zhang & Xiao-Hua Zhou, 2013. "Joint Modeling of Transitional Patterns of Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-11, September.
    4. Shanshan Li, 2016. "Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 145-160, January.
    5. Melody S. Goodman & Yi Li & Anne M. Stoddard & Glorian Sorensen, 2014. "Analysis of ordinal outcomes with longitudinal covariates subject to missingness," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 1040-1052, May.
    6. Paul S. Albert & Dean A. Follmann & Shaohua A. Wang & Edward B. Suh, 2002. "A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness," Biometrics, The International Biometric Society, vol. 58(3), pages 631-642, September.
    7. Xie, Hui, 2012. "Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1287-1300.
    8. Mark Rooij, 2018. "Transitional modeling of experimental longitudinal data with missing values," 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. 12(1), pages 107-130, March.
    9. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre & Yves Vanrenterghem, 2008. "A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros," Biometrics, The International Biometric Society, vol. 64(2), pages 611-619, June.

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