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  • Maternity length of stay modelling by Gamma mixture regression with random effects

    Author(s)
    Lee, Andy H
    Wang, Kui
    Yau, Kelvin KW
    McLachlan, Geoffrey J
    Ng, Shu Kay
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2007
    Metadata
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    Abstract
    Maternity length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed. A two-component gamma mixture regression model has been proposed to analyze the heterogeneous maternity LOS. The problem is that observations collected from the same hospital are often correlated, which can lead to spurious associations and misleading inferences. To account for the inherent correlation, random effects are incorporated within the linear predictors of the two-component gamma mixture regression model. An EM algorithm is developed for the residual maximum quasi-likelihood ...
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    Maternity length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed. A two-component gamma mixture regression model has been proposed to analyze the heterogeneous maternity LOS. The problem is that observations collected from the same hospital are often correlated, which can lead to spurious associations and misleading inferences. To account for the inherent correlation, random effects are incorporated within the linear predictors of the two-component gamma mixture regression model. An EM algorithm is developed for the residual maximum quasi-likelihood estimation of the regression coefficients and variance component parameters. The approach enables the correct identification and assessment of risk factors affecting the short-stay and long-stay patient subgroups. In addition, the predicted random effects can provide information on the inter-hospital variations after adjustment for patient characteristics and health provision factors. A simulation study shows that the estimators obtained via the EM algorithm perform well in all the settings considered. Application to a set of maternity LOS data for women having obstetrical delivery with multiple complicating diagnoses is illustrated.
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    Journal Title
    Biometrical Journal
    Volume
    49
    Issue
    5
    Publisher URI
    http://www.wiley-vch.de/publish/en/journals/alphabeticIndex/2221/?sID=f6o1nqi46t219n3oos183ippu2
    DOI
    https://doi.org/10.1002/bimj.200610371
    Copyright Statement
    © 2007 John Wiley & Sons, Ltd. Self-archiving of the author-manuscript version is not yet supported by this publisher. Please refer to the journal link for access to the definitive, published version or contact the author for more information.
    Subject
    Statistics
    Publication URI
    http://hdl.handle.net/10072/20786
    Collection
    • Journal articles

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