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Change point models for cognitive tests using semi-parametric maximum likelihood

Random-effects change point models are formulated for longitudinal data obtained from cognitive tests. The conditional distribution of the response variable in a change point model is often assumed to be normal even if the response variable is discrete and shows ceiling effects. For the sum score of...

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Detalles Bibliográficos
Autores principales: van den Hout, Ardo, Muniz-Terrera, Graciela, Matthews, Fiona E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: North-Holland Pub. Co 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3587404/
https://www.ncbi.nlm.nih.gov/pubmed/23471297
http://dx.doi.org/10.1016/j.csda.2012.07.024
Descripción
Sumario:Random-effects change point models are formulated for longitudinal data obtained from cognitive tests. The conditional distribution of the response variable in a change point model is often assumed to be normal even if the response variable is discrete and shows ceiling effects. For the sum score of a cognitive test, the binomial and the beta-binomial distributions are presented as alternatives to the normal distribution. Smooth shapes for the change point models are imposed. Estimation is by marginal maximum likelihood where a parametric population distribution for the random change point is combined with a non-parametric mixing distribution for other random effects. An extension to latent class modelling is possible in case some individuals do not experience a change in cognitive ability. The approach is illustrated using data from a longitudinal study of Swedish octogenarians and nonagenarians that began in 1991. Change point models are applied to investigate cognitive change in the years before death.