Modeling the effect of time-dependent exposure on intensive care unit mortality.
OBJECTIVE
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.


CONCLUSIONS
This paper might help to encourage researchers working in hospital epidemiology to apply adequate statistical models to complex medical questions.
Wolkewitz
Martin
M
Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Stefan-Meier-Strasse 26, 79104 Freiburg, Germany. wolke@fdm.uni-freiburg.de
Beyersmann
Jan
J
Gastmeier
Petra
P
Schumacher
Martin
M
D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009026:Mortality / Q000639:trends*