Random survival forests for competing risks

被引：197

作者：

Ishwaran, Hemant ^{[1
]}

Gerds, Thomas A. ^{[2
]}

Kogalur, Udaya B. ^{[3
]}

Moore, Richard D. ^{[4
]}

Gange, Stephen J. ^{[5
]}

Lau, Bryan M. ^{[5
]}

机构：

[1] Univ Miami, Div Biostat, Miami, FL 33136 USA

[2] Univ Copenhagen, Dept Biostat, DK-1014 Copenhagen, Denmark

[3] Cleveland Clin, Dept Quantitat Hlth Sci, Cleveland, OH 44195 USA

[4] Johns Hopkins Univ, Sch Med, Dept Med, Baltimore, MD 21205 USA

[5] Johns Hopkins Univ, Dept Epidemiol, Bloomberg Sch Publ Hlth, Baltimore, MD 21205 USA

来源：

BIOSTATISTICS | 2014年 / 15卷 / 04期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

AIDS; Brier score; Competing risks; C-index; Cumulative incidence function; Ensemble; SELECTION; MODEL;

D O I：

10.1093/biostatistics/kxu010

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection in high-dimensional problems and in settings such as HIV/AIDS that involve many competing risks.

引用

收藏

页码：757 / 773

页数：17

相关论文

共 50 条

[21] Extreme Behavior of Competing Risks with Random Sample Size [J].

Bai, Long ;

Hu, Kaihao ;

Wen, Conghua ;

Tan, Zhongquan ;

Ling, Chengxiu .

AXIOMS, 2024, 13 (08)

[22] A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data [J].

Haile, S. R. ;

Jeong, J. -H. ;

Chen, X. ;

Cheng, Y. .

JOURNAL OF APPLIED STATISTICS, 2016, 43 (12) :2239-2253

[23] Introduction to the Analysis of Survival Data in the Presence of Competing Risks [J].

Austin, Peter C. ;

Lee, Douglas S. ;

Fine, Jason P. .

CIRCULATION, 2016, 133 (06) :601-609

[24] Extending the flexible parametric survival model for competing risks [J].

Hinchliffe, Sally R. ;

Lambert, Paul C. .

STATA JOURNAL, 2013, 13 (02) :344-355

[25] Competing risks model for clustered data based on the subdistribution hazards with spatial random effects [J].

Momenyan, Somayeh ;

Ahmadi, Farzane ;

Poorolajal, Jalal .

JOURNAL OF APPLIED STATISTICS, 2022, 49 (07) :1802-1820

[26] A semiparametric random effects model for multivariate competing risks data [J].

Scheike, Thomas H. ;

Sun, Yanqing ;

Zhang, Mei-Jie ;

Jensen, Tina Kold .

BIOMETRIKA, 2010, 97 (01) :133-145

[27] Causal survival analysis under competing risks using longitudinal modified treatment policies [J].

Diaz, Ivan ;

Hoffman, Katherine L. ;

Hejazi, Nima S. .

LIFETIME DATA ANALYSIS, 2024, 30 (01) :213-236

[28] Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring [J].

Shen, Pao-sheng .

COMPUTATIONAL STATISTICS, 2022, 37 (01) :29-42

[29] A dependent Dirichlet process model for survival data with competing risks [J].

Yushu Shi ;

Purushottam Laud ;

Joan Neuner .

Lifetime Data Analysis, 2021, 27 :156-176

[30] Parametric Regression Approach for Gompertz Survival Times with Competing Risks [J].

Rehman, H. ;

Chandra, N. .

COMMUNICATIONS ON APPLIED MATHEMATICS AND COMPUTATION, 2022, 4 (04) :1175-1190

← 1 2 3 4 5 →