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

Journal of Biopharmaceutical Statistics

Volumn 27, Issue 4, 2017, Pages 620-638

  Tang, Yongqiang ^a  

^a Shire ^*  (United States)

Author keywords

Controlled imputations; delta adjusted imputations; Markov chain Monte Carlo; mixed effects model for repeated measures; monotone data augmentation

Indexed keywords

ANTIDEPRESSANT AGENT;

ALGORITHM; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; COVARIANCE; HUMAN; LINEAR REGRESSION ANALYSIS; MARKOV CHAIN; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PATTERN MIXTURE MODEL; PRIORITY JOURNAL; STATISTICAL DISTRIBUTION; CLINICAL TRIAL (TOPIC); STATISTICAL MODEL;

ALGORITHMS; ANTIDEPRESSIVE AGENTS; BAYES THEOREM; CLINICAL TRIALS AS TOPIC; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD;

EID: 84974698238     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2016.1167075     Document Type: Article

References (24)

1. 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.
2. 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.
3. CHMP, 2010. EMA Guideline on missing data in confirmatory clinical trials (EMA/CPMP/EWP/1776/99). London:CHAMP.
4. 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.
5. 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.
6. 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.
7. Little, R., Yau, L., 1996. Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 52:1324–1333.
8. Little, R. J. A., (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90:1112–1121.
9. 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.
10. 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.
11. Lu, K., 2014. An analytic method for the placebo-based pattern-mixture model. Statistics in Medicine 33:1134–1145.
12. Lu, K., (2015). Power calculations for delta-adjusted pattern-mixture models. Statistics in Medicine 34(5):782–795.
13. 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.
14. Meng, X., (1994). Multiple-imputation inference with uncongenial sources of input. Statistical Science 9:538–573.
15. 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.
16. Nielsen, S., (2003). Proper and improper multiple imputation. International Statistical Review 71:593–627.
17. 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.
18. Schafer, J. L., (1997). Analysis of incomplete multivariate data. London:Chapman Hall.
19. Schafer, J. L., Graham, J. W., (2002). Missing data:Our view of the state of the art. Psychological Methods 7(2):147–177.
20. 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.
21. 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.
22. Tang, Y., (2015a). An efficient monotone data augmentation algorithm for Bayesian analysis of incomplete longitudinal data. Statistics and Probability Letters 104:146–52.
23. Tang, Y., (2015b). Short notes on maximum likelihood inference for control-based pattern-mixture models. Pharmaceutical Statistics 14(5):395–399.
24. Tierney, L., (1994). Markov chains for exploring posterior distributions. Annals of Statistics 22:1701–1728.