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Annexes 213A limit of the study is that we considered missing data as ignorable and we did not accountfor dropout in our model whereas dropout is very frequent in population-based aging studies.Moreover, it is known that missing data and dropout occur more often among impairedsubjects [12] and can be informative. Not accounting for informative missing data can bias theestimations of cognitive declines even if, by using jointly various psychometric tests andadjusting on vascular factors which may be associated with dropout, their impact could bediminished.Another important point is that the latent common factor in this model is defined according tothe pool of psychometric tests used in the analysis. Computing the model with other testsinvolving different cognitive components could have an impact on the common factorevolution. This is why, for this analysis, we selected tests which both are frequently used andexplore different domains of cognition.In spite of these limitations, this methodology allowed us firstly to model jointly four widelyused psychometric tests, including non Gaussian scores or scores with floor and ceilingeffects like the MMSE, and secondly to separate and quantify the differential effects of genderand educational level on the psychometric tests and their global effect on the latent cognitionrepresented by the common factor of the four tests.AcknowledgementsThis study was funded by NOVARTIS pharma, SCOR insurance, Agrica, Conseil régionald’Aquitaine, Conseil Général de la Gironde and Conseil Général de la Dordogne.Disclosure statementThere is no actual or potential conflicts of interest concerning this study.15