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Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout

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  • Joseph W. Hogan
  • Xihong Lin
  • Benjamin Herman

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  • Joseph W. Hogan & Xihong Lin & Benjamin Herman, 2004. "Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout," Biometrics, The International Biometric Society, vol. 60(4), pages 854-864, December.
    • Handle: RePEc:bla:biomet:v:60:y:2004:i:4:p:854-864
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2004.00240.x
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    References listed on IDEAS

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    1. Paul S. Albert & Dean A. Follmann, 2000. "Modeling Repeated Count Data Subject to Informative Dropout," Biometrics, The International Biometric Society, vol. 56(3), pages 667-677, September.
    2. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    3. Rotnitzky Andrea & Daniel Scharfstein & Ting‐Li Su & James Robins, 2001. "Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of Censoring," Biometrics, The International Biometric Society, vol. 57(1), pages 103-113, March.
    4. Michael J. Daniels & Joseph W. Hogan, 2000. "Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout," Biometrics, The International Biometric Society, vol. 56(4), pages 1241-1248, December.
    5. Michael J. Daniels, 2002. "Bayesian analysis of covariance matrices and dynamic models for longitudinal data," Biometrika, Biometrika Trust, vol. 89(3), pages 553-566, August.
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    Cited by:

    1. Joseph Hogan, 2009. "Comments on: Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 59-64, May.
    2. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    3. Angelo F. Elmi & Katherine L. Grantz & Paul S. Albert, 2018. "An approximate joint model for multiple paired longitudinal outcomes and time‐to‐event data," Biometrics, The International Biometric Society, vol. 74(3), pages 1112-1119, September.
    4. Wen Ye & Jeremy M.G. Taylor & Xihong Lin, 2010. "The authors replied as follows:," Biometrics, The International Biometric Society, vol. 66(3), pages 987-991, September.
    5. Ying Yuan & Roderick J. A. Little, 2009. "Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout," Biometrics, The International Biometric Society, vol. 65(2), pages 478-486, June.

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