An efficient monotone data augmentation algorithm for multiple imputation in a class of pattern mixture models

References

• Ayele, B. T., Lipkovich, I., Molenberghs, G., Mallinckrodt, C. H. (2014). A multiple-imputation-based approach to sensitivity analyses and effectiveness assessments in longitudinal clinical trials. Journal of Biopharmaceutical Statistics 24(2):211–28.
   PubMed Web of Science ®Google Scholar
• Carpenter, J., Roger, J., Kenward, M. (2013). Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics 23:1352–71.
   PubMed Web of Science ®Google Scholar
• CHMP, 2010. EMA Guideline on missing data in confirmatory clinical trials (EMA/CPMP/EWP/1776/99). London: CHAMP.
   Google Scholar
• Daniels, M. J., Hogan, J. W., (2008). Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. Boca Raton, FL: Chapman and Hall.
   Google Scholar
• Daniels, M. J., Kass, R. E. (1999). Nonconjugate Bayesian estimation of covariance matrices and its use in hierarchical models. Journal of the American Statistical Association 94:1254–1263.
   Web of Science ®Google Scholar
• Hogan, J. W., Laird, N. M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 16:239–257.
   PubMed Web of Science ®Google Scholar
• Little, R., Yau, L. 1996. Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 52:1324–1333.
   PubMed Web of Science ®Google Scholar
• Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90:1112–1121.
   Web of Science ®Google Scholar
• Liu, J. S. (1994). The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. Journal of the American Statistical Association 89:958–966.
   Web of Science ®Google Scholar
• Liu, J. S., Wong, W. H., Kong, A., (1994). Covariance structure of the Gibbs sampler with applications to comparisons of estimators and augmentation schemes. Biometrika 81:27–40.
   Web of Science ®Google Scholar
• Lu, K., 2014. An analytic method for the placebo-based pattern-mixture model. Statistics in Medicine 33:1134–1145.
   PubMed Web of Science ®Google Scholar
• Lu, K. (2015). Power calculations for delta-adjusted pattern-mixture models. Statistics in Medicine 34(5):782–795.
   PubMed Web of Science ®Google Scholar
• Mallinckrodt, C., Roger, J., Chuang-stein, C., Molenberghs, G., Lane, P. W., Okelly, M., Ratitch, B., Xu, L., Gilbert, S., Mehrotrak, D. V., Wolfinger, R., Thijs, H. (2013). Missing data: Turning guidance into action. Statistics in Biopharmaceutical Research 5:369–382.
   Web of Science ®Google Scholar
• Meng, X. (1994). Multiple-imputation inference with uncongenial sources of input. Statistical Science 9:538–573.
   Web of Science ®Google Scholar
• National Research Council. (2010). Panel on handling missing data in clinical trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. The prevention and treatment of missing data in clinical trials. Washington, DC: The National Academies Press.
   Google Scholar
• Nielsen, S. (2003). Proper and improper multiple imputation. International Statistical Review 71:593–627.
   Web of Science ®Google Scholar
• Ratitch, B., O’Kelly, M., Tosiello, R. (2013). Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models. Pharmaceutical Statistics 12:337–47.
   PubMed Web of Science ®Google Scholar
• Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman Hall.
   Google Scholar
• Schafer, J. L., Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods 7(2):147–177.
   PubMed Web of Science ®Google Scholar
• Siddiqui, O., Hung, J. H. M., O’Neill, R. (2009). MMRM vs. LOCF: A comprehensive comparison based on simulation study and 25 NDA datasets. Journal of Biopharmaceutical Statistics 19:227–246.
   PubMed Web of Science ®Google Scholar
• Sinharay, S. (2004). Experiences with Markov chain Monte Carlo convergence assessment in two psychometric examples. Journal of Educational and Behavioral Statistics 29(4):461–488.
   Web of Science ®Google Scholar
• Tang, Y., (2015a). An efficient monotone data augmentation algorithm for Bayesian analysis of incomplete longitudinal data. Statistics and Probability Letters 104:146–52.
   Web of Science ®Google Scholar
• Tang, Y., (2015b). Short notes on maximum likelihood inference for control-based pattern-mixture models. Pharmaceutical Statistics 14(5):395–399.
   Web of Science ®Google Scholar
• Tierney, L. (1994). Markov chains for exploring posterior distributions. Annals of Statistics 22:1701–1728.
   Web of Science ®Google Scholar