Journal article
OBLIQUE RANDOM SURVIVAL FORESTS
The annals of applied statistics, v 13(3), pp 1847-1883
01 Sep 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 (AS-CVD; 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 oblique RSF 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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Details
- Title
- OBLIQUE RANDOM SURVIVAL FORESTS
- Creators
- Byron C. Jaeger - Univ Alabama Birmingham, Dept Biostat, 327K RYALS Publ Hlth Bldg,1665 Univ Blvd, Birmingham, AL 35294 USAD. Leann Long - Univ Alabama Birmingham, Dept Biostat, 327K RYALS Publ Hlth Bldg,1665 Univ Blvd, Birmingham, AL 35294 USADustin M. Long - Univ Alabama Birmingham, Dept Biostat, 327K RYALS Publ Hlth Bldg,1665 Univ Blvd, Birmingham, AL 35294 USAMario Sims - Univ Mississippi, Med Ctr, Dept Med, Jackson, MS 39216 USAJeff M. Szychowski - Univ Alabama Birmingham, Dept Biostat, 327K RYALS Publ Hlth Bldg,1665 Univ Blvd, Birmingham, AL 35294 USAYuan Min - Univ Mississippi, Med Ctr, Dept Med, Jackson, MS 39216 USALeslie A. Mcclure - Drexel Univ, Dornsife Sch Publ Hlth, Philadelphia, PA 19104 USAGeorge Howard - Univ Alabama Birmingham, Dept Biostat, 327K RYALS Publ Hlth Bldg,1665 Univ Blvd, Birmingham, AL 35294 USANoah Simon - Univ Washington, Dept Biostat, Seattle, WA 98195 USA
- Publication Details
- The annals of applied statistics, v 13(3), pp 1847-1883
- Publisher
- Inst Mathematical Statistics
- Number of pages
- 37
- Grant note
- HHSN268201800014I / Tougaloo College from the National Heart, Lung, and Blood Institute (NHLBI) HHSN268201800010I; HHSN268201800011I; HHSN268201800012I / University of Mississippi Medical Center from the National Institute for Minority Health and Health Disparities (NIMHD) U01 NS041588 / National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Service; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Neurological Disorders & Stroke (NINDS) HHSN268201800015I/HHSN26800001 / Mississippi State Department of Health from the National Institute for Minority Health and Health Disparities (NIMHD) R01 HL080477 / National Heart, Lung and Blood Institute; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) HHSN268201800015I/HHSN26800001 / Mississippi State Department of Health from the National Heart, Lung, and Blood Institute (NHLBI) HHSN268201800013I / Jackson State University from the National Institute for Minority Health and Health Disparities (NIMHD) HHSN268201800013I / Jackson State University from the National Heart, Lung, and Blood Institute (NHLBI) HHSN268201800014I / Tougaloo College from the National Institute for Minority Health and Health Disparities (NIMHD) HHSN268201800010I; HHSN268201800011I; HHSN268201800012I / University of Mississippi Medical Center from the National Heart, Lung, and Blood Institute (NHLBI)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000490874300020
- Scopus ID
- 2-s2.0-85073799138
- Other Identifier
- 991019168196004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability