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Longitudinal factor analysis models and mixture of time-varying random coefficients and their application to the multidimensional assessment of chronic pain
Jeudi 14 novembre 2024, 10:15 à 11:15
Salle des séminaires (M.0.1)
Yousri Slaoui
Université de Poitiers
Multivariate longitudinal data are used in a variety of research areas not only because they allow to analyze time trajectories of multiple indicators, but also to determine how these trajectories are influenced by other covariates. In this article, we
propose a mixture of longitudinal factor analyzers. This model could be used to extract latent factors representing multiple longitudinal noisy indicators in heterogeneous longitudinal data and to study the impact of one or several covariates on these latent factors. One of the advantages of this model is that it allows for measurement non-invariance, which arises in practice when the factor structure varies between groups of individuals due to cultural or physiological
differences. This is achieved by estimating different factor models for different latent classes. The proposed model could also be used to extract latent classes with different latent factor trajectories over time. Other advantages of the model include its ability to take into account heteroscedasticity of errors in the factor analysis model by estimating different error variances for different latent classes. We first define the mixture of longitudinal factor analyzers and its parameters. Then, we propose an EM algorithm to estimate these parameters. We propose a Bayesian information criterion to identify both the number of components in the mixture and the number of latent factors. We then discuss the comparability of the latent factors obtained between subjects in different latent groups. Finally, we apply the model to simulated and real data of patients with chronic postoperative pain.
Travail en collaboration avec Amine Ounajim, Pierre-Yves Louis, Denis Frasca et Philippe Rigoard.
Références :
A. Ounajim, Y. Slaoui, M. Billot and Ph. Rigoard.
Chacun sa souffrance - l’évaluation des douleurs chroniques par les statistiques,
Microscoop - Juin, (2022).
A. Ounajim, Y. Slaoui, P-Y, Louis, M. Billot, D. Frasca and Ph. Rigoard.
Mixture of longitudinal factor analyzers and their application to the assessment of chronic pain, Statistics in Medicine, 42, 3259–3282. (2023).
Y. Slaoui and A. Ounajim.
Évaluation personnalisée et multidimensionnelle de la douleur par des modèles de classification basé sur l'analyse factorielle longitudinale,
INSMI - Octobre, (2023).
https://www.insmi.cnrs.fr/fr/cnrsinfo/evaluation-personnalisee-et-multidimensionnelle-de-la-douleur-par-des-modeles-de
