Smooth random change point models.

Ardo van den Hout, Graciela Muniz-Terrera, Fiona E Matthews
Author Information
  1. Ardo van den Hout: MRC Biostatistics Unit, Institute of Public Health, Cambridge, U.K. ardo.vandenhout@mrc-bsu.cam.ac.uk

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.

Grants

  1. MC_U105292687/Medical Research Council
  2. U.1052.00.013.00001/Medical Research Council
  3. WBS U.1052.00.013.00003/Medical Research Council

MeSH Term

Aged
Aging
Bayes Theorem
Cognition
Cohort Studies
Data Interpretation, Statistical
Humans
Likelihood Functions
Longitudinal Studies
Models, Statistical
Stochastic Processes

Word Cloud

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