A robustified modeling approach to analyze pediatric length of stay
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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
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.
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