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    Penalized spline joint models for longitudinal and time-to-event data

    Access Status
    Fulltext not available
    Authors
    Huong, P.T.T.
    Nur, Darfiana
    Branford, A.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Huong, P.T.T. and Nur, D. and Branford, A. 2017. Penalized spline joint models for longitudinal and time-to-event data. Communications in Statistics - Theory and Methods. 46 (20): pp. 10294-10314.
    Source Title
    Communications in Statistics - Theory and Methods
    DOI
    10.1080/03610926.2016.1235195
    ISSN
    0361-0926
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/79610
    Collection
    • Curtin Research Publications
    Abstract

    The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models.

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