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September 2019 Oblique random survival forests
Byron C. Jaeger, D. Leann Long, Dustin M. Long, Mario Sims, Jeff M. Szychowski, Yuan-I Min, Leslie A. Mcclure, George Howard, Noah Simon
Ann. Appl. Stat. 13(3): 1847-1883 (September 2019). DOI: 10.1214/19-AOAS1261


We introduce and evaluate the oblique random survival forest (ORSF). The ORSF is an ensemble method for right-censored survival data that uses linear combinations of input variables to recursively partition a set of training data. Regularized Cox proportional hazard models are used to identify linear combinations of input variables in each recursive partitioning step. Benchmark results using simulated and real data indicate that the ORSF’s predicted risk function has high prognostic value in comparison to random survival forests, conditional inference forests, regression and boosting. In an application to data from the Jackson Heart Study, we demonstrate variable and partial dependence using the ORSF and highlight characteristics of its ten-year predicted risk function for atherosclerotic cardiovascular disease events (ASCVD; stroke, coronary heart disease). We present visualizations comparing variable and partial effect estimation according to the ORSF, the conditional inference forest, and the Pooled Cohort Risk equations. The obliqueRSF R package, which provides functions to fit the ORSF and create variable and partial dependence plots, is available on the comprehensive R archive network (CRAN).


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Byron C. Jaeger. D. Leann Long. Dustin M. Long. Mario Sims. Jeff M. Szychowski. Yuan-I Min. Leslie A. Mcclure. George Howard. Noah Simon. "Oblique random survival forests." Ann. Appl. Stat. 13 (3) 1847 - 1883, September 2019.


Received: 1 November 2018; Revised: 1 April 2019; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145978
MathSciNet: MR4019160
Digital Object Identifier: 10.1214/19-AOAS1261

Keywords: cardiovascular disease , machine learning , penalized regression , Random forest , survival

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 3 • September 2019
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