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    Jan 26, 2021
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    An alternative classification to mixture modeling for longitudinal counts or binary measures

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    Subtil - 2014 - An alternative ...
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    Authors
    Subtil, F
    Boussari, O
    Bastard, M
    Etard, J-F
    Ecochard, R
    Génolini, C
    Issue Date
    2014-09-01
    
    Metadata
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    Journal
    Statistical Methods in Medical Research
    Abstract
    Classifying patients according to longitudinal measures, or trajectory classification, has become frequent in clinical research. The k-means algorithm is increasingly used for this task in case of continuous variables with standard deviations that do not depend on the mean. One feature of count and binary data modeled by Poisson or logistic regression is that the variance depends on the mean; hence, the within-group variability changes from one group to another depending on the mean trajectory level. Mixture modeling could be used here for classification though its main purpose is to model the data. The results obtained may change according to the main objective. This article presents an extension of the k-means algorithm that takes into account the features of count and binary data by using the deviance as distance metric. This approach is justified by its analogy with the classification likelihood. Two applications are presented with binary and count data to show the differences between the classifications obtained with the usual Euclidean distance versus the deviance distance.
    Publisher
    SAGE Publications
    URI
    http://hdl.handle.net/10144/336371
    DOI
    10.1177/0962280214549040
    PubMed ID
    25179548
    Language
    en
    ISSN
    1477-0334
    ae974a485f413a2113503eed53cd6c53
    10.1177/0962280214549040
    Scopus Count
    Collections
    Research Methods

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