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A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data

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  • Chenguang Wang
  • Michael J. Daniels

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  • Chenguang Wang & Michael J. Daniels, 2011. "A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data," Biometrics, The International Biometric Society, vol. 67(3), pages 810-818, September.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:3:p:810-818
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    References listed on IDEAS

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    1. M. G. Kenward, 2003. "Pattern-mixture models with proper time dependence," Biometrika, Biometrika Trust, vol. 90(1), pages 53-71, March.
    2. Jiameng Zhang & Daniel F. Heitjan, 2006. "A Simple Local Sensitivity Analysis Tool for Nonignorable Coarsening: Application to Dependent Censoring," Biometrics, The International Biometric Society, vol. 62(4), pages 1260-1268, December.
    3. Ofer Harel & Joseph L. Schafer, 2009. "Partial and latent ignorability in missing-data problems," Biometrika, Biometrika Trust, vol. 96(1), pages 37-50.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Yongqiang Tang, 2017. "On the multiple imputation variance estimator for control‐based and delta‐adjusted pattern mixture models," Biometrics, The International Biometric Society, vol. 73(4), pages 1379-1387, December.
    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. Tian Li & Julian M. Somers & Xiaoqiong J. Hu & Lawrence C. McCandless, 2019. "Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 184-205, April.
    4. Antonio R. Linero & Michael J. Daniels, 2015. "A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 45-55, March.
    5. 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.
    6. Wang, Y. & Daniels, M.J., 2013. "Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 130-140.

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