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Bibliographie 172Lin, H., McCulloch, C. E. et Rosenheck, R. A. (2004). Latent pattern mixture modelsfor informative intermittent missing data in longitudinal studies. Biometrics 60,295–305.Lin, H., McCulloch, C. E., Turnbull, B. W., Slate, E. H. et Clark, L. C. (2000).A latent class mixed model for analysing biomarker trajectories with irregularlyscheduled observations. Stat Med 19, 1303–18.Lin, H., Turnbull, B. W., Mc Culloch, C. E. et Slate, E. H. (2002). Latent classmodels for joint analysis of longitudinal biomarker and event process data : applicationto longitudinal prostate-specific antigen readings and prostate cancer.Journal of the American Statistical Association 97, 53–65.Lindstrom, M. J. et Bates, D. M. (1988). Newton-raphson and EM algorithms forlinear mixed-effects models for repeated-measures data. Journal of the AmericanStatistical Association 83, 1014–1022.Little, R. J. (1995). Modeling the drop-out mechanism in repeated-measures studies.Journal of the American Statistical Association 90, 1112–1121.Little, R. J. et Rubin, D. B. (1987). Statistical analysis with missing data. JohnWiley and Sons, New-York.Liu, L. C. et Hedeker, D. (2006). A mixed-effects regression model for longitudinalmultivariate ordinal data. Biometrics 62, 261–8. Journal Article.Lo, Y., Mendell, N. R. et Rubin, D. B. (2001). Testing the number of componentsin a normal mixture. Biometrika 88, 767–778.Lobo, A., Launer, L. J., Fratiglioni, L., Andersen, K., Di Carlo, A., Breteler, M. M.,Copeland, J. R., Dartigues, J. F., Jagger, C., Martinez-Lage, J., Soininen, H.et Hofman, A. (2000). Prevalence of dementia and major subtypes in Europe :A collaborative study of population-based cohorts. Neurologic Diseases in theElderly Research Group. Neurology 54, S4–9.Luchsinger, J. A. et Mayeux, R. (2004). Dietary factors and alzheimer’s disease.Lancet Neurol 3, 579–87.Marshall, G., De la Cruz-Mesia, R., Baron, A. E., Rutledge, J. H. et Zerbe, G. O.