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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Modelling survival in acute severe illness: Cox versus accelerated failure time models
John L Moran1, Andrew D Bersten, Patricia J Solomon
1Department of Intensive Care Medicine, The Queen Elizabeth Hospital, Woodville, SA, Australia. john.moran@nwahs.sa.gov.au
Journal of Evaluation in Clinical Practice
|January 24, 2008
Summary
The Cox model effectively models survival in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) patients, even with time-dependent factors. This approach is crucial for understanding critical illness survival.
Area of Science:
- Critical Care Medicine
- Biostatistics
- Epidemiology
Background:
- The Cox proportional hazards model is standard for survival analysis in critically ill patients.
- Time-dependent covariates are infrequently used in survival analyses for this population.
Purpose of the Study:
- To model 28-day survival in patients with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS).
- To compare the performance of Cox and accelerated failure time (AFT) models in this context.
Main Methods:
- A prospective cohort study of 168 adult ALI patients in Australian ICUs.
- Survival time was measured and censored at 28 days.
- Model performance assessed using goodness-of-fit and explained variation (R2).
Main Results:
- Peak mortality hazard occurred 7-8 days post-ALI/ARDS onset.
- Cox model identified age, female gender, illness severity, and organ dysfunction as predictors, with time-varying effects.
- Cox model showed good specification (GOF, P > 0.34) and R2 = 0.546.
- AFT models identified similar predictors but did not show superior fit.
Conclusions:
- Time-dependent predictors significantly influence survival in ALI/ARDS.
- The Cox model with time-varying covariates is a flexible and appropriate tool for survival analysis in critically ill patients.
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