Hospital Length of Stay: Mean or Median Regression
Length of stay (LOS) is an important measure of hospital activity and
health care utilization, but its empirical distribution is often
positively skewed.
Median regression appears to be a suitable alternative to analyze
the clustered and positively skewed LOS, without transforming and
trimming the data arbitrarily.
Objective. This study reviews the mean and median regression
approaches for analyzing LOS, which have implications for service
planning, resource allocation, and bed utilization.
Methods. The two approaches are applied to analyze hospital discharge
data on cesarean delivery. Both models adjust for patient and
health-related characteristics, and for the dependency of LOS outcomes
nested within hospitals. The estimation methods are also compared in a
simulation study.
Results. For the empirical application, the mean regression results
are somewhat sensitive to the magnitude of trimming chosen. The
identified factors from median regression, namely number of diagnoses,
number of procedures, and payment classification, are robust to
high-LOS outliers. The simulation experiment shows that median
regression can outperform mean regression even when the response
variable is moderately positively skewed.
Conclusion. Median regression appears to be a suitable alternative to
analyze the clustered and positively skewed LOS, without transforming
and trimming the data arbitrarily.
Analyzing Hospital Length of Stay: Mean or Median Regression ?
Medical Care. 41(5):681-686, May 2003.
Lee, Andy H.; Fung, Wing K.; Fu, Bo
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