  We jointly model longitudinal values of a psychometric test and diagnosis of
dementia. The model is based on a continuous-time latent process representing
cognitive ability. The link between the latent process and the observations is
modeled in two phases. Intermediate variables are noisy observations of the
latent process; scores of the psychometric test and diagnosis of dementia are
obtained by categorizing these intermediate variables. We propose maximum
likelihood inference for this model and we propose algorithms for performing
this task. We estimated the parameters of such a model using the data of the
five-year follow-up of the PAQUID study. In particularThis analysis yielded
interesting results about the effect of educational level on both latent
cognitive ability and specific performance in the mini mental test examination.
The predictive ability of the model is illustrated by predicting diagnosis of
dementia at the eight-year follow-up of the PAQUID study bsed on the
information of the first five years.
