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  • Probabilistic subgroup identification using Bayesian finite mixture modelling: A case study in Parkinson's disease phenotype identification

    Author(s)
    White, Nicole
    Johnson, Helen
    Silburn, Peter
    Mellick, George
    Dissanayaka, Nadeeka
    Mengersen, Kerrie
    Griffith University Author(s)
    Silburn, Peter A.
    Mellick, George
    Dissanayaka, Nadeeka
    Year published
    2012
    Metadata
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    Abstract
    This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person's membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups ...
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    This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person's membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson's disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson's Disease Rating Scale (UPDRS).
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    Journal Title
    Statistical Methods in Medical Research
    Volume
    21
    Issue
    6
    DOI
    https://doi.org/10.1177/0962280210391012
    Subject
    Neurology and Neuromuscular Diseases
    Statistics
    Public Health and Health Services
    Publication URI
    http://hdl.handle.net/10072/49795
    Collection
    • Journal articles

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