Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model

Stat Methods Med Res. 2016 Apr;25(2):706-15. doi: 10.1177/0962280212465165. Epub 2012 Nov 6.


There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable.

Keywords: Hospital standardised mortality ratios; hierarchical standardised mortality ratios; mixed-effects logistic regression; non-hierarchical hospital standardised mortality ratios.

Publication types

  • Comparative Study

MeSH terms

  • Diagnosis-Related Groups
  • Gestational Age
  • Hospital Mortality*
  • Humans
  • Infant, Newborn
  • Intensive Care Units, Neonatal
  • Logistic Models*