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Annexes 198Psychometric Tests’ Sensitivity to Cognitive Changes 7ACKNOWLEDGMENTSThis study was funded by Novartis AG, SCOR insuranceAgrica, Conseil Général de la Gironde, and Conseil généralde la Dordogne.Conflict of interest: none declared.REFERENCES1. Morris MC, Evans DA, Hebert LE, et al. Methodological issuesin the study of cognitive decline. Am J Epidemiol 1999;149:789–93.2. Yesavage JA, Brooks JO 3rd. On the importance of longitudinalresearch in Alzheimer’s disease. J Am Geriatr Soc 1991;39:942–4.3. Galasko DR, Gould RL, Abramson IS, et al. Measuring cognitivechange in a cohort of patients with Alzheimer’s disease.Stat Med 2000;19:1421–32.4. Proust C, Jacqmin-Gadda H, Taylor JM, et al. A nonlinearmodel with latent process for cognitive evolution using multivariatelongitudinal data. Biometrics. (Advance Access:doi: 10.1111/j.1541-0420.2006.00573.x.).5. Dartigues JF, Commenges D, Letenneur D, et al. Cognitivepredictors of dementia in elderly community residents. Neuroepidemiology1997;16:29–39.6. Folstein MF, Folstein SE, McHugh PR. ‘‘Mini-Mental State.’’A practical method for grading the cognitive state of patientsfor the clinician. J Psychiatr Res 1975;12:189–98.7. Tombaugh TN, McIntyre NJ. The Mini-Mental State Examination:a comprehensive review. J Am Geriatr Soc 1992;40:922–35.8. Commenges D, Gagnon M, Letenneur L, et al. Statistical descriptionof the Mini-Mental State Examination for Frenchelderly community residents. Paquid Study Group. J NervMent Dis 1992;180:28–32.9. Baker F. The basics of Item Response Theory. College Park,MD: ERIC Clearinghouse on Assessment and Evaluation,2001.10. Letenneur L, Commenges D, Dartigues JF, et al. Incidence ofdementia and Alzheimer’s disease in elderly community residentsof south-western France. Int J Epidemiol 1994;23:1256–61.11. Isaacs B, Kennie AT. The Set test as an aid to the detectionof dementia in old people. Br J Psychiatry 1973;123:467–70.12. Benton A. Manuel pour l’application du Test de RétentionVisuelle. Applications cliniques et expérimentales. (In French).Paris, France: Centre de Psychologie appliquée, 1965.13. Wechsler D. WAIS-R manual. New York, NY: PsychologicalCorporation, 1981.14. Laird NM, Ware JH. Random-effects models for longitudinaldata. Biometrics 1982;38:963–74.15. Hall CB, Lipton RB, Sliwinski M, et al. A change point modelfor estimating the onset of cognitive decline in preclinicalAlzheimer’s disease. Stat Med 2000;19:1555–66.16. Amieva H, Jacqmin-Gadda H, Orgogozo JM, et al. The9 year cognitive decline before dementia of the Alzheimertype: a prospective population-based study. Brain 2005;128:1093–101.17. Jacqmin-Gadda H, Fabrigoule C, Commenges D, et al.A 5-year longitudinal study of the Mini-Mental State Examinationin normal aging. Am J Epidemiol 1997;145:498–506.18. Salthouse TA. The processing-speed theory of adult age differencesin cognition. Psychol Rev 1996;103:403–28.19. Jacqmin-Gadda H, Commenges D, Dartigues J. Analysis oflongitudinal Gaussian data with missing data on the responsevariable. (In French). Rev Epidemiol Sante Publique 1999;47:525–34.20. Little R. Pattern-mixture models for multivariate incompletedata. J Am Stat Assoc 1993;88:125–34.APPENDIXModel specificationWe consider K neuropsychological tests. For each testk, k ¼ 1, ...,K, each subject i, i ¼ 1, ...,N, and each occasionj, j ¼ 1, ...,n ik , the measure of the neuropsychologicaltest y ijk is collected at time t ijk , t ijk being different for eachtest and each subject. The latent process that representsthe common factor of the K neuropsychological tests ismodeled by use of the following linear mixed model, includinga quadratic function of time and a Brownian motion(w i (t)) t0 with variance term r 2 w 3 t:K i ðtÞ¼ðl 0 þ u 0i Þþðl 1 þ u 1i Þ3t þðl 2 þ u 2i Þ3t 2 þ w i ðtÞ:The vector of random effects u i ¼ (u 0i ,u 1i ,u 2i ) T follows amultivariate normal distribution with mean vector 0 and variancecovariance matrix D. The mean evolution of the commonfactor is represented by the fixed effects l 0 , l 1 , and l 2 .The observed score value y ijk is linked to the value of thecommon factor at the time of measurement K i (t ijk ) througha nonlinear link function h k that is a beta cumulative distributionfunction depending on two test-specific parametersh k ¼ (g 1k , g 2k ). This leads to the following measurementmodel:h k ðy ijk ;g k Þ¼K i ðt ijk Þþa ik þ e ijkwhere the test-specific random intercept a ik follows a Gaussiandistribution with mean 0 and variance r 2 ak : It takes intoaccount the residual individual variability between tests afteradjustment on the latent common factor, that is, the fact thattwo subjects with the same latent cognition can score differentlyat the psychometric tests. At last, e ijk are independentGaussian errors with mean 0 and variance r 2 ek :
Annexes 199Impact of gender and education on cognitive decline with age:a longitudinal approachCécile Proust-Lima a,b , Hélène Amieva b,c , Hélène Jacqmin-Gadda a,b,* , Luc Letenneur b,c , Jean-Marc Orgogozo b,c and Jean-François Dartigues b,ca INSERM, EMI 0338 (Biostatistique), Bordeaux , F-33076, FRANCEb Université Victor Segalen Bordeaux 2, Bordeaux , F-33076, FRANCEcINSERM, U 593, Bordeaux , F-33076, FRANCE* correspondence to : Hélène Jacqmin-Gadda, INSERM E0338, ISPED, Université deBordeaux2, 146 rue Léo Saignat, 33076 Bordeaux Cedex, France. Tel : (33) 5 57 57 4518 ;Fax : (33) 5 56 24 00 81 ; e-mail : helene.jacqmin-gadda@bordeaux.inserm.fr1
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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 612.4.3 Cas
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Page 164 and 165: Chapitre 7BibliographieAmieva, H.,
Page 166 and 167: Bibliographie 1661, S19-25.Brown, E
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Page 178 and 179: Bibliographie 178in the random-effe
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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 196 and 197: Annexes 196Psychometric Tests’ Se
Page 200 and 201: Annexes 200Abstract :When investiga
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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
Page 214 and 215: Annexes 214Appendix : model specifi
Page 216 and 217: Annexes 216References[1] Amieva H,
Page 218 and 219: Annexes 218[19] Letenneur L, Commen
Page 220 and 221: Annexes 220Figure 1 : (A) Predicted
Page 222 and 223: Annexes 222Table 1: demographic and
Page 224: Annexes 224MMSE 0.0037 * 0.0013,0.0
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