Télécharger le texte intégral - ISPED-Enseignement à distance

Modèle nonlinéaire à processus latent 71Nonlinear Model with Latent Process for Cognitive Evolution 1019The test-specific random effects α ik dramatically improvedthe fit (574 increase of the log likelihood for four additionalparameters), which means that for a same value of latentcognition, subjects score differently in cognitive domains associatedwith the psychometric tests. Accounting for the withinsubjectvariability with a Brownian motion was also relevantsince it increased the log likelihood of 13.8.Gender was not significantly associated with the mean commonfactor level, while subjects who graduated from primaryschool had a significantly better mean common factor level.Inclusion of contrasts between tests for gender improved significantlythe fit of the model, showing that gender doesnot have the same impact on each psychometric test: mentend to perform better on the BVRT than women, while thetrend is reversed for the other tests. Contrasts between testsfor educational level are not significant, which suggests thatthe effect of educational level does not differ from test totest.The apoE genotype was only included in the latent processevolution (equation (1)) because the hypothesis to evaluatewas an association between the ɛ4 allele and the decline oflatent cognitive performance. We had no hypothesis regard-ing a link with a specific psychometric measure. We found noassociation between the ɛ4 allele and the mean level of thecommon factor at age 65 years but found a strong association(p = 0.0018) with the change over time of the common factor:ɛ4 carriers have a steeper decline than ɛ4 noncarriers asshown in Figure 4a. The model including both the interactionsapoE × t and apoE × t 2 had exactly the same likelihood asthe model including only apoE × t 2 . Thus we retained thelatter.Figure 2 displays the estimated beta transformations forthe four tests with the 95% pointwise confidence interval computedusing a bootstrap method. The four estimated transformationsare very different: the curve is convex for the MMSEand the BVRT, concave for the DSSTW, and close to linearfor the IST. Moreover, the BVRT and the MMSE scorescover, respectively, only 80% and 88% of the latent processrange while the DSSTW covers around 95% and the IST coversalmost the entire range.These results suggest that the MMSE and the BVRT arenot appropriate to identify small changes in cognition amongsubjects with a high cognitive level, because the maximumscores of these tests are reached for a value of the latent110.80.8g(MMSE)0.60.4g(IST)0.60.40.20.200 5 10 15 20 25 30MMSE100 5 10 15 20 25 30 35 40IST10.80.8g(BVRT)0.60.4g(DSSTW)0.60.40.20.200 2 4 6 8 10 12 14BVRT00 10 20 30 40 50 60 70DSSTWFigure 2. Estimated beta transformation for each test (solid line) and the 95% pointwise confidence interval (dashed line,obtained by bootstrap).