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Member Research & Reports

Member Research & Reports

UAB: Oblique Random Survival Forests

A team of researchers, including Drs. Byron Jaeger, Leann Long, Dustin Long, Jeff Szychowski, and George Howard, all from the Department of Biostatistics, University of Alabama at Birmingham School of Public Health, introduced and evaluated 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 were used to identify linear combinations of input variables in each recursive partitioning step.

Benchmark results using simulated and real data indicated 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, researchers demonstrated variable and partial dependence using the ORSF and highlighted characteristics of its ten-year predicted risk function for atherosclerotic cardiovascular disease events (ASCVD; stroke, coronary heart disease).

UAB researchers also presented visualizations comparing variable and partial effect estimation according to the ORSF, the conditional inference forest, and the Pooled Cohort Risk equations. The obliqueRSFR 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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