A robustified modeling approach to analyze pediatric length of stay
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/22296 |
| _version_ | 1848750830597963776 |
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| author | Lee, Andy Gracey, Michael Wang, Kui Yau, K. |
| author_facet | Lee, Andy Gracey, Michael Wang, Kui Yau, K. |
| author_sort | Lee, Andy |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:43:04Z |
| format | Journal Article |
| id | curtin-20.500.11937-22296 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:43:04Z |
| publishDate | 2005 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-222962019-02-19T05:35:00Z A robustified modeling approach to analyze pediatric length of stay Lee, Andy Gracey, Michael Wang, Kui Yau, K. outliers linear mixed model Gastroenteritis transformation robust regression 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. 2005 Journal Article http://hdl.handle.net/20.500.11937/22296 10.1016/j.annepidem.2004.10.001 Elsevier fulltext |
| spellingShingle | outliers linear mixed model Gastroenteritis transformation robust regression Lee, Andy Gracey, Michael Wang, Kui Yau, K. A robustified modeling approach to analyze pediatric length of stay |
| title | A robustified modeling approach to analyze pediatric length of stay |
| title_full | A robustified modeling approach to analyze pediatric length of stay |
| title_fullStr | A robustified modeling approach to analyze pediatric length of stay |
| title_full_unstemmed | A robustified modeling approach to analyze pediatric length of stay |
| title_short | A robustified modeling approach to analyze pediatric length of stay |
| title_sort | robustified modeling approach to analyze pediatric length of stay |
| topic | outliers linear mixed model Gastroenteritis transformation robust regression |
| url | http://hdl.handle.net/20.500.11937/22296 |