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

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

doi:10.1016/S0167-9473(02)00148-2

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 journal › Article › peer-review

72 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	91-109
Number of pages	19
Journal	Computational Statistics and Data Analysis
Volume	42
Issue number	1-2
DOIs	https://doi.org/10.1016/S0167-9473(02)00148-2
State	Published - Feb 19 2003

Keywords

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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10.1016/S0167-9473(02)00148-2

Cite this

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title = "Bayesian and profile likelihood change point methods for modeling cognitive function over time",

abstract = "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.",

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AU - Ying, Jun

AU - Kuo, Lynn

AU - Lipton, Richard B.

N1 - Funding Information: This research supported in part by National Institute on Aging grants AG-03949 and AG-13631.

PY - 2003/2/19

Y1 - 2003/2/19

N2 - 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.

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KW - Bayesian analyses

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KW - Longitudinal data

KW - Markov chain Monte Carlo

KW - Mixed models

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

Abstract

Keywords

ASJC Scopus subject areas

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