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Models for longitudinal data with censored changepoints

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  • Christopher H. Jackson
  • Linda D. Sharples

Abstract

Summary. In longitudinal studies of biological markers, different individuals may have different underlying patterns of response. In some applications, a subset of individuals experiences latent events, causing an instantaneous change in the level or slope of the marker trajectory. The paper presents a general mixture of hierarchical longitudinal models for serial biomarkers. Interest centres both on the time of the event and on levels of the biomarker before and after the event. In observational studies where marker series are incomplete, the latent event can be modelled by a survival distribution. Risk factors for the occurrence of the event can be investigated by including covariates in the survival distribution. A combination of Gibbs, Metropolis–Hastings and reversible jump Markov chain Monte Carlo sampling is used to fit the models to serial measurements of forced expiratory volume from lung transplant recipients.

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  • Christopher H. Jackson & Linda D. Sharples, 2004. "Models for longitudinal data with censored changepoints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 149-162, January.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:1:p:149-162
    DOI: 10.1046/j.0035-9254.2003.05116.x
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    Cited by:

    1. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    2. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.

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