Competing risks models and time-dependent covariates

Nick Graves,Adrian Barnett

doi:10.1186/cc6840

Abstract

New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data.

Highlights

In the present issue of Critical Care Wolkewitz and colleagues use competing risks models to examine risk factors for nosocomial pneumonia and mortality in an intensive care unit [1]
A good example from the study by Wolkewitz and colleagues is elective surgery before admission, which increased the risk of nosocomial pneumonia but decreased the risk of death and discharge
A timedependent covariate is one that changes during the study period; for example, ventilation

Summary

Introduction

In the present issue of Critical Care Wolkewitz and colleagues use competing risks models to examine risk factors for nosocomial pneumonia and mortality in an intensive care unit [1]. Competing risks models offer significant advantages over standard survival analysis [2]. In the study by Wolkewitz and colleagues there were three competing risks: nosocomial pneumonia, death and discharge. Covariates can depend on the competing risk.

Results

Conclusion