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  • Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach

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    Author(s)
    Ng, Shu Kay
    Tawiah, Richard
    Mclachlan, Geoffrey J
    Gopalan, Vinod
    Griffith University Author(s)
    Gopalan, Vinod
    Ng, Shu Kay Angus
    Year published
    2021
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    Abstract
    Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is ...
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    Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is based on a joint frailty modeling approach with multivariate random effects to account for the heterogeneous risk of failure and the presence of informative censoring due to a terminal event. We develop a generalized linear mixed model method for the efficient estimation of parameters. We demonstrate the capacity of our approach using a real cancer registry data set on the multimorbidity of melanoma patients and document the relative performance of the proposed joint frailty model to the natural competitor of a standard frailty model via extensive simulation studies. Our new approach is timely to advance evidence-based knowledge to address increasingly complex needs related to multimorbidity and develop interventions that are most effective and viable to better help a large number of individuals with multiple conditions.
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    Journal Title
    Biostatistics
    DOI
    https://doi.org/10.1093/biostatistics/kxab037
    Copyright Statement
    © The Author 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Biostatistics
    Genetics
    Statistics
    Cancer registry data
    Generalized linear mixed models
    Informative censoring
    Mean residual life
    Multimorbidity
    Publication URI
    http://hdl.handle.net/10072/410196
    Collection
    • Journal articles

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