Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout
dc.contributor.author | Yuan, Ying | en_US |
dc.contributor.author | Little, Roderick J. A. | en_US |
dc.date.accessioned | 2010-04-01T15:36:44Z | |
dc.date.available | 2010-04-01T15:36:44Z | |
dc.date.issued | 2009-06 | en_US |
dc.identifier.citation | Yuan, Ying; Little, Roderick J. A. (2009). "Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout." Biometrics 65(2): 478-486. <http://hdl.handle.net/2027.42/66099> | en_US |
dc.identifier.issn | 0006-341X | en_US |
dc.identifier.issn | 1541-0420 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/66099 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18759842&dopt=citation | en_US |
dc.description.abstract | Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process and dropout process is factorized into the marginal distribution of random effects, the dropout process conditional on random effects, and the outcome process conditional on dropout patterns and random effects. MEHMs combine features of selection models and pattern-mixture models: they directly model the missingness process as in selection models, and enjoy the computational simplicity of pattern-mixture models. The MEHM provides a generalization of shared-parameter models (SPMs) by relaxing the conditional independence assumption between the measurement process and the dropout process given random effects. Because SPMs are nested within MEHMs, likelihood ratio tests can be constructed to evaluate the conditional independence assumption of SPMs. We use data from a pediatric AIDS clinical trial to illustrate the models. | en_US |
dc.format.extent | 220655 bytes | |
dc.format.extent | 3110 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Inc | en_US |
dc.rights | ©2009 International Biometric Society | en_US |
dc.subject.other | Longitudinal Data | en_US |
dc.subject.other | Missing Data | en_US |
dc.subject.other | Nonignorable Dropout | en_US |
dc.subject.other | Shared-parameter Model | en_US |
dc.title | Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, U.S.A. | en_US |
dc.identifier.pmid | 18759842 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/66099/1/j.1541-0420.2008.01102.x.pdf | |
dc.identifier.doi | 10.1111/j.1541-0420.2008.01102.x | en_US |
dc.identifier.source | Biometrics | en_US |
dc.identifier.citedreference | Agresti, A. ( 2002 ). Analysis of Categorical Data, 2nd edition. New York : Wiley. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | De Gruttola, V. and Tu, X. M. ( 1994 ). Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics 50, 1003 – 1014. | en_US |
dc.identifier.citedreference | Diggle, P. and Kenward, M. G. ( 1994 ). Informative drop-out in longitudinal data analysis. Applied Statistics 43, 49 – 73. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | Follman, D. and Wu, M. C. ( 1995 ). An approximate generalized linear model with random effects for informative missing data. Biometrics 51, 151 – 168. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | Hogan, J. W., Roy, J., and Korkontzelou, C. ( 2004 ). Biostatistics tutorial: Handling dropout in longitudinal data. Statistics in Medicine 23, 1455 – 1497. | en_US |
dc.identifier.citedreference | Laird, N. M. and Ware, J. H. ( 1982 ). Random-effects models for longitudinal data. Biometrics 38, 963 – 974. | en_US |
dc.identifier.citedreference | Little, R. J. A. ( 1993 ). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125 – 134. | en_US |
dc.identifier.citedreference | Little, R. J. A. ( 1994 ). A class of pattern mixture models for normal missing data. Biometrika 81, 471 – 483. | en_US |
dc.identifier.citedreference | Little, R. J. A. ( 1995 ). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112 – 1121. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | Little, R. J. A. and Rubin, D. B. ( 2002 ). Statistical Analysis with Missing Data, 2nd edition. New York : Wiley. | en_US |
dc.identifier.citedreference | Michiels, B., Molenberghs, G., and Lipsitz, S. R. ( 1999 ). Selection models and pattern-mixture models for incomplete data with covariates. Biometrics 55, 978 – 983. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | Verbeke, G. and Molenberghs, G. ( 2000 ). Linear Mixed Models for Longitudinal Data. New York : Springer-Verlag. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.identifier.citedreference | 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. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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