Mixed-effect hybrid models for longitudinal data with nonignorable dropout

Biometrics

Volumn 65, Issue 2, 2009, Pages 478-486

  Yuan, Ying ^a   Little, Roderick J A ^b  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Longitudinal data; Missing data; Nonignorable dropout; Shared parameter model

Indexed keywords

HYBRID MODEL; LONGITUDINAL DATA; MISSING DATA; MIXED EFFECTS; MIXTURE MODELING; NONIGNORABLE DROPOUT; PARAMETER MODEL; RANDOM EFFECTS; SELECTION MODEL; SHARED-PARAMETER MODEL;

RANDOM PROCESSES;

HYBRID; MEASUREMENT METHOD; MODELING;

ACQUIRED IMMUNE DEFICIENCY SYNDROME; ARTICLE; MIXED EFFECT HYBRID MODEL; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; STATISTICAL MODEL;

EID: 63049121747     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01102.x     Document Type: Article

References (26)

1. Agresti, A. (2002). Analysis of Categorical Data, 2nd edition. New York : Wiley.
2. Brady, M. T., McGrath, N., Brouwers, P., et al., for the Pediatric AIDS clinical trial 1996). Randomized study of the tolerance and efficacy of high- versus low-dose zidovudine in human immunodeficiency virus-infected children with mild to moderate symptoms (ACTG 128). Journal of Infectious Disease 173, 1097 1106. (Pubitemid 26251451)
3. De Gruttola, V. and Tu, X. M. (1994). Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics 50, 1003 1014.
4. Diggle, P. and Kenward, M. G. (1994). Informative drop-out in longitudinal data analysis. Applied Statistics 43, 49 73.
5. Fitzmaurice, G. M., Laird, N. M., and Schneyer, L. (2001). An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs. Statistics in Medicine 20, 1009 1021.
6. Follman, D. and Wu, M. C. (1995). An approximate generalized linear model with random effects for informative missing data. Biometrics 51, 151 168.
7. Hogan, J. W. and Laird, N. M. (1997a). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 16, 239 257. (Pubitemid 27029300)
8. Hogan, J. W. and Laird, N. M. (1997b). Model-based approaches to analysing incomplete longitudinal and failure time data. Statistics in Medicine 16, 259 272. (Pubitemid 27029301)
9. Hogan, J. W., Lin, X., and Herman, B. (2004). Mixture of varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout. Biometrics 60, 854 864. (Pubitemid 40019026)
10. Hogan, J. W., Roy, J., and Korkontzelou, C. (2004). Biostatistics tutorial: Handling dropout in longitudinal data. Statistics in Medicine 23, 1455 1497. (Pubitemid 38594149)
11. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963 974. (Pubitemid 14272804)
12. Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125 134.
13. Little, R. J. A. (1994). A class of pattern mixture models for normal missing data. Biometrika 81, 471 483.
14. Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112 1121.
15. Little, R. J. A. (2008). Selection and pattern-mixture models. In Advances in Longitudinal Data Analysis, G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs (eds). London : CRC Press.
16. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd edition. New York : Wiley.
17. Michiels, B., Molenberghs, G., and Lipsitz, S. R. (1999). Selection models and pattern-mixture models for incomplete data with covariates. Biometrics 55, 978 983. (Pubitemid 29427109)
18. Pinheiro, J. C. and Bates, D. M. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical Statistics 4, 12 35.
19. Pulkstenis, E., Ten Have, T. R., and Landis, J. R. (1998). Model for the analysis of binary longitudinal pain data subject to informative dropout through remedication. Journal of the American Statistical Association 93, 438 450.
20. Robins, J., Rotnitzky, A., and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106 121.
21. Rotnitzky, A., Robins, J. M., and Scharfstein, D. O. (1998). Semiparametric regression for repeated outcomes with non-ignorable non-response. Journal of the American Statistical Association 93, 1321 1339. (Pubitemid 128385545)
22. Scharfstein, D., Robins, J., and Rotnitzky, A. (1999). Adjusting for nonignorable nonresponse using semiparametric nonresponse models with time dependent covariates (with discussion). Journal of the American Statistical Association 94, 1096 1146.
23. Ten Have, T. R., Pulkstenis, E., Kunselman, A., and Landis, J. R. (1998). Mixed effects logistic regression models for longitudinal binary response data with informative dropout. Biometrics 54, 367 383. (Pubitemid 28158811)
24. Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York : Springer-Verlag.
25. Wu, M. C. and Bailey, K. R. (1989). Estimation and comparison of changes in the presence of informative right censoring: Conditional linear model. Biometrics 45, 939 955. (Pubitemid 19249332)
26. Wu, M. C. and Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 44, 175 188. (Pubitemid 18098821)