Bayesian and profile likelihood change point methods for modeling cognitive function over time

Charles B. Hall, Jun Ying, Lynn Kuo, Richard B. Lipton

Research output: Contribution to journalArticle

62 Scopus citations


Change point models are often used to model longitudinal data. To estimate the change point, Bayesian (Biometrika 62 (1975) 407; Appl. Statist. 41 (1992) 389; Biometrics 51 (1995) 236) or profile likelihood (Statist. Med. 19 (2000) 1555) methods may be used. We compare and contrast the two methods in analyzing longitudinal cognitive data from the Bronx Aging Study. The Bayesian method has advantages over the profile likelihood method in that it does not require all subjects to have the same change point. Caution must be taken regarding sensitivity to choice of prior distribution, identifiability, and goodness of fit. Analyses show that decline in memory precedes diagnosis of dementia by 7.5-8 years, and individual change points are not needed to model heterogeneity across subjects.

Original languageEnglish (US)
Pages (from-to)91-109
Number of pages19
JournalComputational Statistics and Data Analysis
Issue number1-2
StatePublished - Feb 19 2003



  • Bayesian analyses
  • Change points
  • Cognitive aging
  • Longitudinal data
  • Markov chain Monte Carlo
  • Mixed models

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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