The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 13, Number 3 (2019), 1847-1883.
Oblique random survival forests
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).
Ann. Appl. Stat., Volume 13, Number 3 (2019), 1847-1883.
Received: November 2018
Revised: April 2019
First available in Project Euclid: 17 October 2019
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Jaeger, Byron C.; Long, D. Leann; Long, Dustin M.; Sims, Mario; Szychowski, Jeff M.; Min, Yuan-I; Mcclure, Leslie A.; Howard, George; Simon, Noah. Oblique random survival forests. Ann. Appl. Stat. 13 (2019), no. 3, 1847--1883. doi:10.1214/19-AOAS1261. https://projecteuclid.org/euclid.aoas/1571277776
- Source code for analyses presented in the oblique random survival forest manuscript. Provides scripts written in R that were applied to generate the results presented in the manuscript. In particular, the scripts were applied to conduct the simulation/resampling study and the application of oblique random survival forests to the Jackon Heart Study.