Modeling the Effect of Time-Dependent Exposure on Intensive Care Unit Mortality

Intensive Care Med. 2009 May;35(5):826-32. doi: 10.1007/s00134-009-1423-6. Epub 2009 Jan 31.


Purpose: To illustrate modern survival models with focus on the temporal dynamics of intensive care data. A typical situation is given in which time-dependent exposures and competing events are present.

Methods: We briefly review the following established statistical methods: logistic regression, regression models for event-specific hazards and the subdistribution hazard. These approaches are compared by showing advantages as well as disadvantages. All methods are applied to real data from a study of day-by-day ICU surveillance.

Results: Standard logistic regression ignores the time-dependent nature of the data and is only a crude approach. Cumulative hazards and probability plots add important information and provide a deep insight into the temporal dynamics.

Conclusion: This paper might help to encourage researchers working in hospital epidemiology to apply adequate statistical models to complex medical questions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Length of Stay / statistics & numerical data*
  • Models, Statistical*
  • Mortality / trends*