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A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data

dc.contributor.authorProust, Cécileen_US
dc.contributor.authorJacqmin-Gadda, Hélèneen_US
dc.contributor.authorTaylor, Jeremy M. G.en_US
dc.contributor.authorGaniayre, Julienen_US
dc.contributor.authorCommenges, Danielen_US
dc.date.accessioned2010-04-01T15:54:21Z
dc.date.available2010-04-01T15:54:21Z
dc.date.issued2006-12en_US
dc.identifier.citationProust, CÉcile; Jacqmin-Gadda, HÉlÈne; Taylor, Jeremy M. G.; Ganiayre, Julien; Commenges, Daniel (2006). "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data." Biometrics 62(4): 1014-1024. <http://hdl.handle.net/2027.42/66403>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/66403
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=17156275&dopt=citationen_US
dc.description.abstractCognition is not directly measurable. It is assessed using psychometric tests, which can be viewed as quantitative measures of cognition with error. The aim of this article is to propose a model to describe the evolution in continuous time of unobserved cognition in the elderly and assess the impact of covariates directly on it. The latent cognitive process is defined using a linear mixed model including a Brownian motion and time-dependent covariates. The observed psychometric tests are considered as the results of parameterized nonlinear transformations of the latent cognitive process at discrete occasions. Estimation of the parameters contained both in the transformations and in the linear mixed model is achieved by maximizing the observed likelihood and graphical methods are performed to assess the goodness of fit of the model. The method is applied to data from PAQUID, a French prospective cohort study of ageing.en_US
dc.format.extent542013 bytes
dc.format.extent3110 bytes
dc.format.mimetypeapplication/pdf
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dc.publisherBlackwell Publishing Incen_US
dc.rights2006, The International Biometric Societyen_US
dc.subject.otherCognitive Ageingen_US
dc.subject.otherMixed Modelen_US
dc.subject.otherMultiple Outcomesen_US
dc.subject.otherRandom Effectsen_US
dc.titleA Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Dataen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109, U.S.A.en_US
dc.contributor.affiliationotherINSERM E0338, UniversitÉ de Bordeaux 2, 146 rue LÉo Saignat, 33076 Bordeaux Cedex, Franceen_US
dc.identifier.pmid17156275en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/66403/1/j.1541-0420.2006.00573.x.pdf
dc.identifier.doi10.1111/j.1541-0420.2006.00573.xen_US
dc.identifier.sourceBiometricsen_US
dc.identifier.citedreferenceAmieva, H., Jacqmin-Gadda, H., Orgogozo, J., LeCarret, N., Helmer, C., Letenneur, L., Barberger-Gateau, P., Fabrigoule, C., and Dartigues, J. ( 2005 ). The 9 year cognitive decline before dementia of the Alzheimer type: A prospective population-based study. Brain 128, 1093 – 1101.en_US
dc.identifier.citedreferenceArminger, G. and MuthÉn, B. ( 1998 ). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm. Psychometrika 63, 271 – 300.en_US
dc.identifier.citedreferenceDunson, D. ( 2000 ). Bayesian latent variable models for clustered mixed outcomes. Journal of the Royal Statistical Society, Series B 62, 355 – 366.en_US
dc.identifier.citedreferenceDunson, D. ( 2003 ). Dynamic latent trait models for multidimensional longitudinal data. Journal of the American Statistical Association 98, 555 – 563.en_US
dc.identifier.citedreferenceFabrigoule, C., Rouch, I., Taberly, A., Letenneur, L., Commenges, D., Mazaux, J.-M., Orgogozo, J.-M., and Dartigues, J.-F. ( 1998 ). Cognitive process in preclinical phase of dementia. Brain 121, 135 – 141.en_US
dc.identifier.citedreferenceFarrer, L., Cupples, L., Haines, J., Hyman, B., Kukull, W., Mayeux, R., Myers, R., Pericak-Vance, M., Risch, N., and Van Duijn, C. ( 1997 ). Effects of age, sex and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. Journal of the American Medical Association 278, 1349 – 1356.en_US
dc.identifier.citedreferenceGray, S. M. and Brookmeyer, R. ( 1998 ). Estimating a treatment effect from multidimensional longitudinal data. Biometrics 54, 976 – 988.en_US
dc.identifier.citedreferenceHall, C., Ying, J., Kuo, L., Sliwinski, M., Buschke, H., Katz, M., and Lipton, R. ( 2001 ). Estimation of bivariate measurements having different change points, with application to cognitive ageing. Statistics in Medicine 20, 3695 – 3714.en_US
dc.identifier.citedreferenceHarvey, D., Beckett, L., and Mungas, D. ( 2003 ). Multivariate modeling of two associated cognitive outcomes in a longitudinal study. Journal of Alzheimer's Disease 5, 357 – 365.en_US
dc.identifier.citedreferenceHashemi, R., Jacqmin-Gadda, H., and Commenges, D. ( 2003 ). A latent process model for joint modeling of events and marker. Lifetime Data Analysis 9, 331 – 343.en_US
dc.identifier.citedreferenceJacqmin-Gadda, H., Fabrigoule, C., Commenges, D., and Dartigues, J.-F. ( 1997 ). A 5-year longitudinal study of the Mini Mental State Examination in normal aging. American Journal of Epidemiology 145, 498 – 506.en_US
dc.identifier.citedreferenceJÖreskog, K. and Yang, F. ( 1996 ). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In Advanced Structural Equation Modeling: Issues and Techniques, G. A. Marcoulides and R. E. Schumacker ( eds ), 57 – 88. Mahwah, New Jersey : Lawrence Erlbaum Associates.en_US
dc.identifier.citedreferenceKendall, M. and Stuart, A. ( 1977 ). The Advanced Theory of Statistics, Volume 1. New York : MacMillan Publishing.en_US
dc.identifier.citedreferenceLee, S. and Song, X. ( 2004 ). Bayesian model comparison of nonlinear structural equation models with missing continuous and ordinal categorical data. British Journal of Mathematical and Statistical Psychology 57, 131 – 150.en_US
dc.identifier.citedreferenceLetenneur, L., Commenges, D., Dartigues, J.-F., and Barberger-Gateau, P. ( 1994 ). Incidence of dementia and Alzheimer's disease in the elderly community residents of south-western France. International Journal of Epidemiology 23, 577 – 590.en_US
dc.identifier.citedreferenceLongford, N. and MuthÉn, B. ( 1992 ). Factor analysis for clustered observations. Psychometrika 57, 581 – 597.en_US
dc.identifier.citedreferenceMarquardt, D. ( 1963 ). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 11, 431 – 441.en_US
dc.identifier.citedreferenceMolenberghs, G. and Verbeke, G. ( 2001 ). A review on linear mixed models for longitudinal data, possibly subject to dropout. Statistical Modelling 1, 235 – 269.en_US
dc.identifier.citedreferenceMuthÉn, B. ( 2002 ). Beyond SEM: General latent variable modeling. Behaviormetrika 29, 81 – 117.en_US
dc.identifier.citedreferenceRabe-Hesketh, S., Skrondal, A., and Pickles, A. ( 2004 ). Generalized multilevel structural equation modelling. Psychometrika 69, 167 – 190.en_US
dc.identifier.citedreferenceRoy, J. and Lin, X. ( 2000 ). Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics 56, 1047 – 1054.en_US
dc.identifier.citedreferenceRoy, J. and Lin, X. ( 2002 ). Analysis of multivariate longitudinal outcomes with nonignorable dropouts and missing covariates: Changes in methadone treatment practices. Journal of the American Statistical Association 97, 40 – 52.en_US
dc.identifier.citedreferenceSalthouse, T., Hancock, H., Meinz, E., and Hambrick, D. ( 1996 ). Interrelations of age, visual acuity, and cognitive functioning. Journal of Gerontology: Psychological Sciences 51B, 317 – 330.en_US
dc.identifier.citedreferenceSammel, M. and Ryan, L. ( 1996 ). Latent variable models with fixed effects. Biometrics 52, 650 – 663.en_US
dc.identifier.citedreferenceSÁnchez, B. N., Budtz-Jorgensen, E., Ryan, L. M., and Hu, H. ( 2005 ). Structural equation models: A review with applications to environmental epidemiology. Journal of the American Statistical Association 100, 1443 – 1455.en_US
dc.identifier.citedreferenceSkrondal, A. and Rabe-Hesketh, S. ( 2004 ). Generalized Latent Variable Modelling: Multilevel, Longitudinal and Structural Equation Models. Boca Raton, Florida: Chapman & Hall/CRC.en_US
dc.identifier.citedreferenceSliwinski, M., Hofer, S., and Hall, C. ( 2003 ). Correlated and coupled cognitive change in older adults with or without preclinical dementia. Psychology and Aging 18, 672 – 683.en_US
dc.identifier.citedreferenceSong, X. and Lee, S. ( 2004 ). Bayesian analysis of two-level nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology 57, 29 – 52.en_US
dc.identifier.citedreferenceWall, M. and Amemiya, Y. ( 2000 ). Estimation for polynomial structural equation models. Journal of the American Statistical Association 95, 929 – 940.en_US
dc.identifier.citedreferenceWinnock, M., Letenneur, L., Jacqmin-Gadda, H., Dallongeville, J., Amouyel, P., and Dartigues, J. ( 2002 ). Longitudinal analysis of the effect of apolipoprotein E ε4 and education on cognitive performance in elderly subjects: The PAQUID study. Journal of Neurology, Neurosurgery and Psychiatry 72, 794 – 797.en_US
dc.identifier.citedreferenceYalcin, I. and Amemiya, Y. ( 2001 ). Nonlinear factor analysis as a statistical method. Statistical Science 16, 275 – 294.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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