Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    A robustified modeling approach to analyze pediatric length of stay

    19819_19819.pdf (147.1Kb)
    Access Status
    Open access
    Authors
    Lee, Andy
    Gracey, Michael
    Wang, Kui
    Yau, K.
    Date
    2005
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Lee, Andy H. and Gracey, Michael and Wang, Kui and Yau, Kelvin K.W. 2005. A robustified modeling approach to analyze pediatric length of stay. Annals of Epidemiology. 15 (9): 673-677.
    Source Title
    Annals of Epidemiology
    DOI
    10.1016/j.annepidem.2004.10.001
    Faculty
    School of Public Health
    Division of Health Sciences
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Annals of Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Annals of Epidemiology, Volume 15, Issue 9, October 2005, Pages 673–677, http://dx.doi.org/doi:10.1016/j.annepidem.2004.10.001

    URI
    http://hdl.handle.net/20.500.11937/22296
    Collection
    • Curtin Research Publications
    Abstract

    PurposeLength of stay (LOS) is an important measure of the cost of pediatric hospitalizations, but the guidelines developed so far are not rigorously evidence-based. This study demonstrates a robust gamma mixed regression approach to analyze the positively skewed LOS variable, which has implications for future studies of pediatric health care management.MethodsThe robustified approach is applied to analyze hospital discharge data on childhood gastroenteritis in Western Australia (n = 514). The model accounts for demographic characteristics and co-morbidities of the patients, as well as the dependency of LOS outcomes nested within the 58 hospitals in the State. The method is compared with the standard linear mixed regression with trimming of extreme observations.ResultsFor the empirical application, the linear mixed regression results are sensitive to the magnitude of trimming. The identified significant factors from the robust regression model, namely infection, failure to thrive, and iron deficiency anemia are resistant to high-LOS outliers.ConclusionsRobust gamma mixed regression appears to be a suitable alternative to analyze the clustered and positively skewed pediatric LOS, without transforming and trimming the data arbitrarily.

    Related items

    Showing items related by title, author, creator and subject.

    • Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review
      Ng, Curtise (2023)
      Artificial intelligence (AI)-based computer-aided detection and diagnosis (CAD) is an important research area in radiology. However, only two narrative reviews about general uses of AI in pediatric radiology and AI-based ...
    • Comparison of Pediatric Severe Sepsis Managed in U.S. and European ICUs
      Giuliano, J.; Markovitz, B.; Brierley, J.; Levin, R.; Williams, G.; Lum, L.; Dorofaeff, T.; Cruces, P.; Bush, J.; Keele, L.; Nadkarni, V.; Thomas, N.; Fitzgerald, J.; Weiss, S.; Fontela, P.; Tucci, M.; Dumistrascu, M.; Skippen, P.; Krahn, G.; Bezares, E.; Puig, G.; Puig-Ramos, A.; Garcia, R.; Villar, M.; Bigham, M.; Polanski, T.; Latifi, S.; Giebner, D.; Anthony, H.; Hume, J.; Galster, A.; Linnerud, L.; Sanders, R.; Hefley, G.; Madden, K.; Thompson, A.; Shein, S.; Gertz, S.; Han, Y.; Williams, Teresa; Hughes-Schalk, A.; Chandler, H.; Orioles, A.; Zielinski, E.; Doucette, A.; Orioles, A.; Zielinski, E.; Doucette, A.; Zebuhr, C.; Wilson, T.; Dimitriades, C.; Ascani, J.; Layburn, S.; Valley, S.; Markowitz, B.; Terry, J.; Morzov, R.; McInnes, A.; McArthur, J.; Woods, K.; Murkowski, K.; Spaeder, M.; Sharron, M.; Wheeler, D.; Beckman, E.; Frank, E.; Howard, K.; Carroll, C.; Nett, S.; Jarvis, D.; Patel, V.; Higgerson, R.; Christie, L.; Typpo, K.; Deschenes, J.; Kirby, A.; Uhl, T.; Rehder, K.; Cheifetz, I.; Wrenn, S.; Kypuros, K.; Ackerman, K.; Maffei, F.; Bloomquist, G.; Rizkalla, N.; Kimura, D.; Shah, S.; Tigges, C.; Su, F.; Barlow, C.; Michelson, K.; Wolfe, K.; Goodman, D.; Campbell, L.; Sorce, L.; Bysani, K.; Monjure, T.; Evans, M.; Totapally, B.; Chegondi, M. (2016)
      Copyright © 2016 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.Objectives: Pediatric severe sepsis remains a significant global health problem without ...
    • Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review
      Ng, Curtise (2023)
      Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.