Mixture survival trees for cancer risk classification

DOI10.1007/s10985-022-09552-wOpenAlexW4225126445MaRDI QIDQ2163807

Donglin Zeng, Beilin Jia, Jason J. Z. Liao, Joseph G. Ibrahim, Guanghan F. Liu, Guoqing Diao, XianMing Tan

Publication date: 11 August 2022

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-022-09552-w

zbMATH Keywords

censoringmixture distributionrisk classificationlatent modeltree-based method

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62Nxx)

Cites Work

• Rationale and applications of survival tree and survival ensemble methods
• \(L_1\) splitting rules in survival forests
• Nonparametric Estimation from Incomplete Observations
• Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model
• Maximum likelihood estimation for semiparametric transformation models with interval-censored data
• Joint Models for Multivariate Longitudinal and Multivariate Survival Data
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