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Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout

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  • Michael J. Daniels
  • Joseph W. Hogan

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  • 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.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:4:p:1241-1248
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.01241.x
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Frederico Poleto & Geert Molenberghs & Carlos Paulino & Julio Singer, 2011. "Sensitivity analysis for incomplete continuous data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 589-606, November.
    2. Michael J. Daniels & Arkendu S. Chatterjee & Chenguang Wang, 2012. "Bayesian Model Selection for Incomplete Data Using the Posterior Predictive Distribution," Biometrics, The International Biometric Society, vol. 68(4), pages 1055-1063, December.
    3. Prajamitra Bhuyan, 2019. "Estimation of random-effects model for longitudinal data with nonignorable missingness using Gibbs sampling," Computational Statistics, Springer, vol. 34(4), pages 1693-1710, December.
    4. 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.
    5. Andrzej S. Kosinski & Huiman X. Barnhart, 2003. "Accounting for Nonignorable Verification Bias in Assessment of Diagnostic Tests," Biometrics, The International Biometric Society, vol. 59(1), pages 163-171, March.
    6. Margarita Moreno-Betancur & Grégoire Rey & Aurélien Latouche, 2015. "Direct likelihood inference and sensitivity analysis for competing risks regression with missing causes of failure," Biometrics, The International Biometric Society, vol. 71(2), pages 498-507, June.
    7. Andrea Gabrio & Michael J. Daniels & Gianluca Baio, 2020. "A Bayesian parametric approach to handle missing longitudinal outcome data in trial‐based health economic evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 607-629, February.
    8. 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.
    9. 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.
    10. Jolene Birmingham & Garrett M. Fitzmaurice, 2002. "A Pattern-Mixture Model for Longitudinal Binary Responses with Nonignorable Nonresponse," Biometrics, The International Biometric Society, vol. 58(4), pages 989-996, December.
    11. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    12. Niko A. Kaciroti & Trivellore E. Raghunathan & M. Anthony Schork & Noreen M. Clark, 2008. "A Bayesian model for longitudinal count data with non‐ignorable dropout," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 521-534, December.

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