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Original Articles

Bayesian Analysis of Hierarchical Pattern-Mixture Models for Clinical Trials Data with Attrition and Comparisons to Commonly Used Ad-Hoc and Model-Based Approaches

Pages 383-402 | Received 24 Dec 2003, Accepted 20 Sep 2004, Published online: 02 Feb 2007

REFERENCES

  • Amemiya , T. ( 1984 ). Tobit models: A survey . J. Econometr. 24 : 3 – 61 .
  • Bates , D. M. , Pinheiro , J. C. ( 1997 ). Computational methods for multilevel modeling. Technical Memorandum . Bell Labs , New Jersey .
  • 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 .
  • 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 .
  • 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 .
  • Demirtas , H. , Schafer , J. L. ( 2003 ). On the performance of random-coefficient pattern-mixture models for nonignorable dropout . Stat. Med. 22 : 2553 – 2575 . [CROSSREF]
  • Diggle , P. J. , Kenward , M. G. ( 1994 ). Informative dropout in longitudinal data analysis (with discussion) . Appl. Statist. 43 : 49 – 94 .
  • 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 . [CROSSREF]
  • Gelfand , A. E. , Smith , A. F. M. ( 1990 ). Sampling-based approaches to calculating marginal densities . J. Am. Stat. Assoc. 85 : 398 – 409 .
  • Gelfand , A. E. , Hills , S. E. , Racine-Poon , A. M. , Smith , A. F. M. ( 1990 ). Illustration of bayesian inference in normal data models using gibbs sampling . J. Am. Stat. Assoc. 85 : 972 – 985 .
  • 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 .
  • Goldstein , H. M. ( 2003 ). Multilevel Statistical Models . London : Arnold .
  • Greene , W. H. ( 2000 ). Econometric Analysis . 4th ed. Upper Saddle River , New Jersey : Prentice-Hall .
  • 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 .
  • Hedeker , D. , Gibbons , R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychol. Methods 2:64–78. [CSA] [CROSSREF]
  • Kenward , M. G. ( 1998 ). Selection models for repeated measurements with nonrandom dropout: An illustration of sensitivity . Stat. Med. 17 : 2723 – 2732 . [CROSSREF]
  • Laird , N. M. , Ware , J. H. ( 1982 ). Random-effects models for longitudinal data . Biometrics 38 : 963 – 974 .
  • Little , R. J. A. ( 1993 ). Pattern-mixture models for multivariate incomplete data . J. Am. Stat. Assoc. 88 : 125 – 134 .
  • Little , R. J. A. ( 1994 ). A class of pattern-mixture models for normal missing data . Biometrika 81 : 471 – 483 .
  • Little , R. J. A. ( 1995 ). Modeling the dropout mechanism in repeated measures studies . J. Am. Stat. Assoc. 90 : 1112 – 1121 .
  • Little , R. J. A. , Rubin , D. B. ( 2002 ). Statistical Analysis with Missing Data . New York : Wiley .
  • Little , R. J. A. , Wang , Y. A. ( 1996 ). Pattern-mixture models for multivariate incomplete data with covariates . Biometrics 52 : 98 – 111 . [CSA]
  • Liu , C. A. , Rubin , D. B. ( 1994 ). The ECME algorithm: A simple extension of EM and ECM with faster convergence . Biometrika 81 : 633 – 648 .
  • Longford , N. T. ( 1995 ). Random Coefficient Models . Oxford : Oxford University Press .
  • Maddala , G. S. ( 1983 ). Limited Dependent and Qualitative Variables in Econometrics. Cambridge : Cambridge University Press .
  • Molenberghs , G. A. , Michiels , B. A. , Kenward , M. G. , Diggle , P. J. ( 1998 ). Missing data mechanisms and pattern-mixture models . Stat. Neerl. 52 : 153 – 161 . [CROSSREF]
  • Nelder , J. A. , Mead , R. A. ( 1965 ). A simplex method for function minimization . Comput. J. 7 : 308 – 313 .
  • 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 .
  • Rubin , D. B. ( 1976 ). Inference and missing data . Biometrika 63 : 581 – 592 .
  • Rubin , D. . ( 1987 ). Multiple Imputation for Nonresponse in Surveys . New York : Wiley .
  • Rubin , D. B. ( 1996 ). Multiple imputation after 18+ years (with discussion) . J. Am. Stat. Assoc. 91 : 473 – 520 .
  • Schafer , J. L. ( 1997 ). Analysis of Incomplete Multivariate Data . London : Chapman & Hall .
  • Schafer , J. L. , Graham , J. W. ( 2002 ). Missing data: Our view of the state of the art . Psychol. Methods 7 : 147 – 177 . [CSA] [CROSSREF]
  • 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 .
  • Verbeke , G. , Molenberghs , G. ( 2000 ). Linear Mixed Models for Longitudinal Data . New York : Springer-Verlag .
  • Wu , M. C. , Bailey , K. R. ( 1989 ). Estimation and comparisons of changes in the presence of informative right censoring . Biometrics 45 : 939 – 955 .
  • Zeger , S. L. , Karim , M. R. ( 1991 ). Generalized linear models with random effects: A Gibbs sampling approach . J. Am. Stat. Assoc. 86 : 79 – 86 .

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