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  • Identifying co-morbidity patterns of health conditions via cluster analysis of pairwise concordance statistics

    Author(s)
    Ng, Shu Kay
    Holden, Libby
    Sun, Jing
    Griffith University Author(s)
    Sun, Jing
    Ng, Shu Kay Angus
    Year published
    2012
    Metadata
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    Abstract
    Identification of co-morbidity patterns of health conditions is critical for evidence- based practice to improve the prevention, treatment, and health care of relevant diseases. Existing approaches focus mainly on either using descriptive measures of co-morbidity in terms of the prevalence of co-existing conditions, or addressing the prevalence of co-morbidity based on a particular disease (e.g. psychosis) or a specific population (e.g. hospital patients). As coincidental co-morbidity by chance increases with the prevalence rates of the conditions, which in turn depend heavily on the population under study, research findings ...
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    Identification of co-morbidity patterns of health conditions is critical for evidence- based practice to improve the prevention, treatment, and health care of relevant diseases. Existing approaches focus mainly on either using descriptive measures of co-morbidity in terms of the prevalence of co-existing conditions, or addressing the prevalence of co-morbidity based on a particular disease (e.g. psychosis) or a specific population (e.g. hospital patients). As coincidental co-morbidity by chance increases with the prevalence rates of the conditions, which in turn depend heavily on the population under study, research findings on co-morbidity patterns using those approaches may provide unreliable results. In this paper, we propose an asymmetric version of Somers' D statistic to provide a quantitative measure of co-morbidity that accounts for co-occurrence of conditions by chance, and develop a unified clustering algorithm to identify co-morbidity patterns with adjustment for multiple testing and control for the false discovery rate. We assess the applicability of the proposed co-morbidity measure and investigate the performance of the proposed procedure for the adjustment of multiple testing by conducting a comparative study and a sensitivity analysis, respectively. The proposed method is illustrated using a national survey data set of mental health and wellbeing and a national health survey data set in Australia. Keywords: asymmetric Somers' D statistic; co-morbidity; concordance statistic; multiplicity problem; national survey data; overlapping clusters.
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    Journal Title
    Statistics in Medicine
    Volume
    31
    Issue
    27
    DOI
    https://doi.org/10.1002/sim.5426
    Subject
    Statistics
    Biostatistics
    Epidemiology not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/46084
    Collection
    • Journal articles

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