Bayesian analysis of hierarchical pattern-mixture models for clinical trials data with attrition and comparisons to commonly used ad-hoc and model-based approaches

Journal of Biopharmaceutical Statistics

Volumn 15, Issue 3, 2005, Pages 383-402

  Demirtas, Hakan ^a  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

Dropout; Multiple imputation; Pattern mixture models; Robustness

Indexed keywords

ARTICLE; BAYES THEOREM; CLINICAL TRIAL; INTERMETHOD COMPARISON; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; CLINICAL TRIALS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; LINEAR MODELS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; PATIENT DROPOUTS; POPULATION; REPRODUCIBILITY OF RESULTS;

EID: 18744401063     PISSN: 10543406     EISSN: None     Source Type: Journal    
DOI: 10.1081/BIP-200056511     Document Type: Article

References (37)

1. Amemiya, T. (1984). Tobit models: A survey. J. Econometr. 24:3-61.
2. Bates, D. M., Pinheiro, J. C. (1997). Computational methods for multilevel modeling. Technical Memorandum. Bell Labs, New Jersey.
3. Carithers, R. L., Herlong, H. F., Diehl, A. M., Shaw, E. W., Combers, B., Fallon, H. J., Maddrey, W. C. (1989). Methylprednisolone therapy in patients with severe alcoholic hepatitis. Ann. Intern. Med. 110:685-690.
4. Carlin, B. P. (1996). Hierarchical longitudinal modeling. In: Gilks, W. R., Richardson, S., Spiegelhalter, D. J. eds. Markov Chain Monte Carlo in Practice. London: Chapman and Hall, pp. 303-319.
5. Demirtas, H. (2003). Multiple Imputation for Nonignorable Dropout Using Bayesian Pattern-Mixture Models. Technical report: 03-58. University Park: The Pennsylvania State University, Prevention Methodology Center.
6. Demirtas, H., Schafer, J. L. (2003). On the performance of random-coefficient pattern-mixture models for nonignorable dropout. Stat. Med. 22:2553-2575.
7. Diggle, P. J., Kenward, M. G. (1994). Informative dropout in longitudinal data analysis (with discussion). Appl. Statist. 43:49-94.
8. Fitzmaurice, G. M., Laird, N. M., Shneyer, L. (2001). An alternative parameterization of the general linear mixture model for longitudinal data with nonignorable dropouts. Stat. Med. 20:1009-1021.
9. Gelfand, A. E., Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85:398-409.
10. Gelfand, A. E., Hills, S. E., Racine-Poon, A., Smith, A. F. M. (1990). Illustration of bayesian inference in normal data models using gibbs sampling. J. Am. Stat. Assoc. 85:972-985.
11. Glynn, R. J., Laird, N. M., Rubin, D. B. (1993). Multiple imputation in mixture models for nonignorable nonresponse with follow-ups. J. Am. Stat. Assoc. 88:984-993.
12. Goldstein, H. (2003). Multilevel Statistical Models. London: Arnold.
13. Greene, W. H. (2000). Econometric Analysis. 4th ed. Upper Saddle River, New Jersey: Prentice-Hall.
14. Heckman, J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables, and a simple estimator for such models. Ann. Econ. Soc. Meas. 5:475-492.
15. Hedeker, D., Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychol. Methods 2:64-78.
16. Kenward, M. G. (1998). Selection models for repeated measurements with nonrandom dropout: An illustration of sensitivity. Stat. Med. 17:2723-2732.
17. Laird, N. M., Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38:963-974.
18. Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. J. Am. Stat. Assoc. 88:125-134.
19. Little, R. J. A. (1994). A class of pattern-mixture models for normal missing data. Biometrika 81:471-483.
20. Little, R. J. A. (1995). Modeling the dropout mechanism in repeated measures studies. J. Am. Stat. Assoc. 90:1112-1121.
21. Little, R. J. A., Rubin, D. B. (2002). Statistical Analysis with Missing Data. New York: Wiley.
22. Little, R. J. A., Wang, Y. (1996). Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 52:98-111.
23. Liu, C., Rubin, D. B. (1994). The ECME algorithm: A simple extension of EM and ECM with faster convergence. Biometrika 81:633-648.
24. Longford, N. T. (1995). Random Coefficient Models. Oxford: Oxford University Press.
25. Maddala, G. S. (1983). Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.
26. Molenberghs, G., Michiels, B., Kenward, M. G., Diggle, P. J. (1998). Missing data mechanisms and pattern-mixture models. Stat. Neerl. 52:153-161.
27. Nelder, J. A., Mead, R. (1965). A simplex method for function minimization. Comput. J. 7:308-313.
28. Raudenbush, S. W. (1993). A crossed random effects model for unbalanced data with applications in cross-sectional and longitudinal research. J. Educ. Stat. 18:321-349.
29. Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581-592.
30. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
31. Rubin, D. B. (1996). Multiple imputation after 18+ years (with discussion). J. Am. Stat. Assoc. 91:473-520.
32. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.
33. Schafer, J. L., Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychol. Methods 7:147-177.
34. Smith, D. M., Robertson, W. H., Diggle, P. J. (1996). Oswald: Object-Oriented Software for the Analysis of Longitudinal Data in S. Technical Report MA 96/192, Department of Mathematics and Statistics, University of Lancaster, United Kingdom.
35. Verbeke, G., Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag.
36. Wu, M. C., Bailey, K. R. (1989). Estimation and comparisons of changes in the presence of informative right censoring. Biometrics 45:939-955.
37. Zeger, S. L., Karim, M. R. (1991). Generalized linear models with random effects: A Gibbs sampling approach. J. Am. Stat. Assoc. 86:79-86.