Classification Trees for Survival Data with Competing Risks

Classification trees are the most popular tool for categorizing individuals into groups and subgroups based on particular outcomes of interest. To date, trees have not been developed for the competing risk situation where survival times are recorded and more than one outcome is possible. In this work we propose three classification trees to analyze survival data with multiple competing risk outcomes, using both univariate and multivariate techniques, respectively. After we describe the method used in growing and pruning the classification trees for competing risks, we demonstrate the performance with simulations in a variety of competing risk model configurations, and compare the competing risk trees to currently available tree-based methods. We also illustrate their use by analyzing survival data concerning patients who had end-stage liver disease and were on the waiting list to receive a liver transplant.Public Health Significance: Competing risks are common in longitudinal studies. The classification tree for competing risks will provide more accurate estimates of risk in distinct subpopulations than the current tree techniques can provide.