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Zero-Inflated Count Regression Models in Solving Challenges Posed by Outlier-Prone Data; an Application to Length of Hospital Stay
- Source :
- Archives of Academic Emergency Medicine, Vol 12, Iss 1 (2023)
- Publication Year :
- 2023
- Publisher :
- Shahid Beheshti University of Medical Sciences, 2023.
-
Abstract
- Introduction: Ignoring outliers in data may lead to misleading results. Length of stay (LOS) is often considered a count variable with a high frequency of outliers. This study exemplifies the potential of robust methodologies in enhancing the accuracy and reliability of analyses conducted on skewed and outlier-prone count data of LOS. Methods: The application of Zero-Inflated Poisson (ZIP) and robust Zero-Inflated Poisson (RZIP) models in solving challenges posed by outlier LOS data were evaluated. The ZIP model incorporates two components, tackling excess zeros with a zero-inflation component and modeling positive counts with a Poisson component. The RZIP model introduces the Robust Expectation-Solution (RES) algorithm to enhance parameter estimation and address the impact of outliers on the model's performance. Results: Data from 254 intensive care unit patients were analyzed (62.2% male). Patients aged 65 or older accounted for 58.3% of the sample. Notably, 38.6% of patients exhibited zero LOS. The overall mean LOS was 5.89 (± 9.81) days, and 9.45% of cases displayed outliers. Our analysis using the RZIP model revealed significant predictors of LOS, including age, underlying comorbidities (p
Details
- Language :
- English
- ISSN :
- 26454904
- Volume :
- 12
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Archives of Academic Emergency Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b53a27434017459a8ac1092c865b7f94
- Document Type :
- article
- Full Text :
- https://doi.org/10.22037/aaem.v12i1.2074