Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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Analysis of multivariate mixed longitudinal data: a flexible latent process approach
Cécile Proust-Lima, Hélène Amieva, Hélène Jacqmin-Gadda

Last modified: 2013-06-14

Abstract


We introduce a flexible latent process model to describe jointly multivariate longitudinal scales measuring the same underlying latent process. The main asset of this model is that it handles multiple types of longitudinal outcomes (quantitative, bounded quantitative outcomes and ordinal) and corrects for their metrological properties (ceiling/floor effects but also curvilinearity i.e varying sensitivity to change). Specifically, we combine the random-effect approach and the latent variable approach: the latent process trajectory is described by a (structural) linear mixed model while measurement models combine outcome-specific threshold models for ordinal outcomes and models based on a series of flexible parameterised nonlinear families of transformations for quantitative outcomes. The assets of the flexible latent process model are highlighted through several applications from a large population-based cognitive ageing study.

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