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Activity Number: 262
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #318865
Title: Random Forests for Survival Analysis Using Maximally Selected Rank Statistics
Author(s): Marvin N. Wright* and Theresa Dankowski and Andreas Ziegler
Companies: University of Lübeck and Universität zu Lübeck and University of Lübeck
Keywords: maximally selected statistics ; random forests ; rank statistics ; survival analysis

The most popular approach for analyzing survival data is the Cox regression model. However, its proportionality assumption is not always fulfilled. An alternative is the use of random forests for survival outcomes. The standard split criterion is the log rank test statistic, which favors splitting variables with many possible split points. In this presentation, we introduce maximally selected rank statistics for split point selection. To avoid split point selection bias, the method minimizes p-values for association between split points and survival time. We describe several p-value approximations and the implementation of the proposed random forests approach. A simulation study demonstrates that unbiased split point selection is possible. However, there is a trade-off between unbiased split point selection and runtime. Benchmark studies of prediction performance on simulated and real datasets on breast cancer show that the new method performs equally well or even better than random survival forests, conditional inference forests and the Cox model. In a runtime comparison the method proves to be computationally faster than random survival forests and conditional inference forests.

Authors who are presenting talks have a * after their name.

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