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Quantile regression with a change‐point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease

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  • Chenxi Li
  • N. Maritza Dowling
  • Rick Chappell

Abstract

Progressive and insidious cognitive decline that interferes with daily life is the defining characteristic of Alzheimer's disease (AD). Epidemiological studies have found that the pathological process of AD begins years before a clinical diagnosis is made and can be highly variable within a given population. Characterizing cognitive decline in the preclinical phase of AD is critical for the development of early intervention strategies when disease‐modifying therapies may be most effective. In the last decade, there has been an increased interest in the application of change‐point models to longitudinal cognitive outcomes prior to and after diagnosis. Most of the proposed statistical methodology for describing decline relies upon distributional assumptions that may not hold. In this article, we introduce a quantile regression with a change‐point model for longitudinal data of cognitive function in persons bound to develop AD. A change‐point in our model reflects the transition from the cognitive decline due to normal aging to the accelerated decline due to disease progression. Quantile regression avoids common distributional assumptions on cognitive outcomes and allows the covariate effects and the change‐point to vary for different quantiles of the response. We provided an approach for estimating the model parameters, including the change‐point, and presented inferential procedures based on the asymptotic properties of the estimators. A simulation study showed that the estimation and inferential procedures perform reasonably well in finite samples. The practical use of our model was illustrated by an application to longitudinal episodic memory outcomes from two cohort studies of aging and AD.

Suggested Citation

  • Chenxi Li & N. Maritza Dowling & Rick Chappell, 2015. "Quantile regression with a change‐point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease," Biometrics, The International Biometric Society, vol. 71(3), pages 625-635, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:625-635
    DOI: 10.1111/biom.12313
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    References listed on IDEAS

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    1. Liu Yuan & Bottai Matteo, 2009. "Mixed-Effects Models for Conditional Quantiles with Longitudinal Data," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-24, November.
    2. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    3. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
    4. Hélène Jacqmin-Gadda & Daniel Commenges & Jean-François Dartigues, 2006. "Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia," Biometrics, The International Biometric Society, vol. 62(1), pages 254-260, March.
    5. Chenxi Li & Ying Wei & Rick Chappell & Xuming He, 2011. "Bent Line Quantile Regression with Application to an Allometric Study of Land Mammals' Speed and Mass," Biometrics, The International Biometric Society, vol. 67(1), pages 242-249, March.
    6. Xuming He, 2002. "Estimation in a semiparametric model for longitudinal data with unspecified dependence structure," Biometrika, Biometrika Trust, vol. 89(3), pages 579-590, August.
    7. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    8. Hall, Charles B. & Ying, Jun & Kuo, Lynn & Lipton, Richard B., 2003. "Bayesian and profile likelihood change point methods for modeling cognitive function over time," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 91-109, February.
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    Cited by:

    1. Alexander Schmidt-Richberg & Christian Ledig & Ricardo Guerrero & Helena Molina-Abril & Alejandro Frangi & Daniel Rueckert & on behalf of the Alzheimer’s Disease Neuroimaging Initiative, 2016. "Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-27, April.

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