A semi-latent class model for estimating the time of differentiation of cognitive decline between cases and controls
- Autores: C. Segalàs, Hélène Jacqmin-Gadda
- Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 422-425
- Idioma: inglés
- Enlaces
-
Resumen
In this work, we propose a semi-latent class random changepoint mixed model that allows the estimation of the time of differentiation between cognitive decline of future demented and normal subjects from a nested-casecontrol study. Cases are assumed to have a random changepoint trajectory while controls can have either a linear trajectory or a random changepoint trajectory where the class membership follows a logistic model. The log-likelihood of the model is derived and can be optimized using a Levenberg-Marquardt algorithm with Gaussian quadrature for numerical integration. The model is estimated on the Paquid cohort of elderly with very long follow-up (25 years) to estimate the delay between the beginning of the decline of a test of verbal uency and the onset of dementia.
