Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies
dc.contributor.author | Liu, Minzhi | en_US |
dc.contributor.author | Taylor, Jeremy M. G. | en_US |
dc.contributor.author | Belin, Thomas R. | en_US |
dc.date.accessioned | 2010-04-01T14:52:31Z | |
dc.date.available | 2010-04-01T14:52:31Z | |
dc.date.issued | 2000-12 | en_US |
dc.identifier.citation | Liu, Minzhi; Taylor, Jeremy M. G.; Belin, Thomas R. (2000). "Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies." Biometrics 56(4): 1157-1163. <http://hdl.handle.net/2027.42/65329> | en_US |
dc.identifier.issn | 0006-341X | en_US |
dc.identifier.issn | 1541-0420 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/65329 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=11213759&dopt=citation | en_US |
dc.description.abstract | This paper outlines a multiple imputation method for handling missing data in designed longitudinal studies. A random coefficients model is developed to accommodate incomplete multivariate continuous longitudinal data. Multivariate repeated measures are jointly modeled; specifically, an i.i.d. normal model is assumed for time-independent variables and a hierarchical random coefficients model is assumed for time-dependent variables in a regression model conditional on the time-independent variables and time, with heterogeneous error variances across variables and time points. Gibbs sampling is used to draw model parameters and for imputations of missing observations. An application to data from a study of startle reactions illustrates the model. A simulation study compares the multiple imputation procedure to the weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90 , 106–121) that can be used to address similar data structures. | en_US |
dc.format.extent | 749973 bytes | |
dc.format.extent | 3110 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Ltd | en_US |
dc.rights | The International Biometric Society, 2000 | en_US |
dc.subject.other | Gibbs Sampling | en_US |
dc.subject.other | Missing Data | en_US |
dc.subject.other | Multiple Imputation | en_US |
dc.subject.other | Multivariate Longitudinal Data | en_US |
dc.title | Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies | 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 | Clinical Biostatistics, Merck and Co., Inc., Rahway, New Jersey 07065, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Biostatistics, UCLA School of Public Health, Los Angeles, California 90095, U.S.A. | en_US |
dc.identifier.pmid | 11213759 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/65329/1/j.0006-341X.2000.01157.x.pdf | |
dc.identifier.doi | 10.1111/j.0006-341X.2000.01157.x | en_US |
dc.identifier.source | Biometrics | en_US |
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dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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