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Chapitre 7BibliographieAmieva, H., Jacqmin-Gadda, H., Orgogozo, J. M., Le Carret, N., Helmer, C., Letenneur,L., Barberger-Gateau, P., Fabrigoule, C. et Dartigues, J. F. (2005). The9 year cognitive decline before dementia of the Alzheimer type : a prospectivepopulation-based study. Brain 128, 1093–101.Andersen, K., Launer, L. J., Dewey, M. E., Letenneur, L., Ott, A., Copeland, J. R.,Dartigues, J. F., Kragh-Sorensen, P., Baldereschi, M., Brayne, C., Lobo, A.,Martinez-Lage, J. M., Stijnen, T. et Hofman, A. (1999). Gender differences in theincidence of AD and vascular dementia : The EURODEM studies. EURODEMIncidence Research Group. Neurology 53, 1992–7.Anstey, K. J., Hofer, S. M. et A., L. M. (2003). A latent growth curve analysis oflate-life sensory and cognitive function ovar 8 years : evidence for specific andcommon factors underlying change. Psychology ang aging 18, 714–726.Backman, L., Small, B. J. et Fratiglioni, L. (2001). Stability of the preclinicalepisodic memory deficit in Alzheimer’s disease. Brain 124, 96–102.Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L. et Rathouz, P. J. (1997). latentvariable regression for multiple discrete outcomes. Journal of the AmericanStatistical Association 92, 1375–1386.Barnes, L. L., Wilson, R. S., Schneider, J. A., Bienias, J. L., Evans, D. A. et Bennett,D. A. (2003). Gender, cognitive decline, and risk of AD in older persons.164
Bibliographie 165Neurology 60, 1777–81.Bauer, D. J. et Curran, P. J. (2003a). Distributional assumptions of growth mixturemodels : implications for overextraction of latent trajectory classes. PsycholMethods 8, 338–63.Bauer, D. J. et Curran, P. J. (2003b). Overextraction of latent trajectory classes :much ado about nothing ? Reply to Rindskopf (2003), Muthén (2003) and Cudeckand Henly (2003). Psychol Methods 8, 384–393.Beckett, L. A., Tancredi, D. J. et Wilson, R. S. (2004). Multivariate longitudinalmodels for complex change processes. Stat Med 23, 231–9.Benton, A. (1965). Manuel pour l’application du Test de Rétention Visuelle. Applicationscliniques et expérimentales. Centre de Psychologie appliquée, Paris,2ème édition française edition.Berchtold, A. (2004). Optimization of mixture models : comparison of differentstrategies. Computational statistics 19, 385–406.Beunckens, C., Molenberghs, G., Verbeke, G. et Mallinckrodt, C. (2006). A latentclassmixture model for incomplete longitudinal gaussian data. Center for statistics,Hasselt University, Belgium.Biernacki, C., Celeux, G. et Govaert, G. (2003). Choosing starting values for theEM algorithm for getting the highest likelihood in multivariate Gaussian mixturemodels. Computational Statistics and Data Analysis 41, 561–575.Bohning, D. (1995). A review of reliable maximum likelihood algorithms for semiparametricmixture models. Journal of Statistical Planning and Inference 47,5–28.Bohning, D. (2000). Computer-assisted analysis of mixtures and applications : metaanalysis,disease mapping and others. Chapmann & Hall/CRC, Boca Raton, FL.Bohning, D. et Seidel, W. (2003). Editorial : recent developments in mixture models.Computational Statistics and Data Analysis 41, 349–357.Brooks, J. O. et Yesavage, J. A. (1995). Identification of fast and slow decliners inAlzheimer disease : a different approach. Alzheimer Dis Assoc Disord 9 Suppl
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Université Victor Segalen Bordeaux
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3RemerciementsA Monsieur Jean-Louis
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5Un immense merci à tous ceux que
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7A Delphine et ses mille et une his
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TABLE DES MATIÈRES 92.3.2 Estimati
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TABLE DES MATIÈRES 116.3.1 Adéqua
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Introduction 13ans atteintes d’un
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Introduction 15Mais, pour l’insta
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Introduction 171.2 Problèmes méth
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Introduction 191.2.3 Association en
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Introduction 21QUID suggère que le
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Chapitre 2Etat des connaissancesCe
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Etat des connaissances 25Plusieurs
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Etat des connaissances 27Prise en c
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Etat des connaissances 29Extensions
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Etat des connaissances 31données s
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Etat des connaissances 33variables
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Etat des connaissances 35(2000) ne
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Etat des connaissances 372.3 Modél
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Etat des connaissances 39chapitre 3
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Etat des connaissances 41de mélang
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Etat des connaissances 43Hawkins et
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Etat des connaissances 45tique de d
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Etat des connaissances 47L’estima
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Etat des connaissances 49normales s
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Etat des connaissances 512.4 Modél
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Etat des connaissances 53en tant qu
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Etat des connaissances 55interactio
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Etat des connaissances 57décrite p
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Etat des connaissances 59entre l’
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Etat des connaissances 612.4.3 Cas
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Etat des connaissances 63l’inform
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Modèle nonlinéaire à processus l
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Chapitre 4Modèle nonlinéaire à c
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Modèle nonlinéaire à classes lat
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Page 166 and 167: Bibliographie 1661, S19-25.Brown, E
Page 168 and 169: Bibliographie 168Sons, New-York.Fol
Page 170 and 171: Bibliographie 170Hogan, J. W. et La
Page 172 and 173: Bibliographie 172Lin, H., McCulloch
Page 174 and 175: Bibliographie 174Park, CA.Muthén,
Page 176 and 177: Bibliographie 176Schlattmann, P. (2
Page 178 and 179: Bibliographie 178in the random-effe
Page 180 and 181: Chapitre 8Annexes8.1 Liste des publ
Page 182 and 183: Annexes 182Pau (France)Communicatio
Page 184 and 185: Annexes 184166 C. Proust, H. Jacqmi
Page 192 and 193: AnnexesAmerican Journal of Epidemio
Page 194 and 195: Annexes 194Psychometric Tests’ Se
Page 200 and 201: Annexes 200Abstract :When investiga
Page 202 and 203: Annexes 202educated subjects. This
Page 204 and 205: Annexes 204(iii) the recognition fo
Page 206 and 207: Annexes 206Explanatory variablesIn
Page 208 and 209: Annexes 208IST15 (median=28, IQR=24
Page 210 and 211: Annexes 210and on the mean evolutio
Page 212 and 213: Annexes 212These findings should be
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Annexes 214Appendix : model specifi
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Annexes 216References[1] Amieva H,
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Annexes 218[19] Letenneur L, Commen
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Annexes 220Figure 1 : (A) Predicted
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Annexes 222Table 1: demographic and
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Annexes 224MMSE 0.0037 * 0.0013,0.0
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