title: Smooth random change point models.
abstract: Change point models are used to describe processes over time that show a change in direction. An example of such a process is cognitive ability, where a decline a few years before death is sometimes observed. A broken-stick model consists of two linear parts and a breakpoint where the two lines intersect. Alternatively, models can be formulated that imply a smooth change between the two linear parts. Change point models can be extended by adding random effects to account for variability between subjects. A new smooth change point model is introduced and examples are presented that show how change point models can be estimated using functions in R for mixed-effects models. The Bayesian inference using WinBUGS is also discussed. The methods are illustrated using data from a population-based longitudinal study of ageing, the Cambridge City over 75 Cohort Study. The aim is to identify how many years before death individuals experience a change in the rate of decline of their cognitive ability.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Cambridge, U.K. ardo.vandenhout@mrc-bsu.cam.ac.uk
authors.forename: Ardo
authors.initials: A
authors.lastname: van den Hout
authors.forename: Graciela
authors.initials: G
authors.lastname: Muniz-Terrera
authors.forename: Fiona E
authors.initials: FE
authors.lastname: Matthews
mesh_terms: D000368:Aged; D000375:Aging / Q000523:psychology; D001499:Bayes Theorem; D003071:Cognition; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D013269:Stochastic Processes