Regression trees and ensembles for cumulative incidence functions

DOI10.1515/IJB-2021-0014WikidataQ113236356 ScholiaQ113236356MaRDI QIDQ6637103

Young Joo Cho, Robert L. Strawderman, Chen Hu, Annette Molinaro

Publication date: 13 November 2024

Published in: The International Journal of Biostatistics (Search for Journal in Brave)

zbMATH Keywords

competing risksrandom forestsCARTcause-specific hazardFine and Gray modelbrier score

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

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• Random survival forests
• A class of k-sample tests for comparing the cumulative incidence of a competing risk
• Tree-based multivariate regression and density estimation with right-censored data
• Quantile regression for competing risks data with missing cause of failure
• Quantifying the predictive accuracy of time-to-event models in the presence of competing risks
• Parametric regression on cumulative incidence function
• Predicting cumulative incidence probability by direct binomial regression
• Linear regression with censored data
• Checking the Cox model with cumulative sums of martingale-based residuals
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Estimating the Mean of an Increasing Stochastic Process at a Censored Stopping Time
• A Partitioning Deletion/Substitution/Addition Algorithm for Creating Survival Risk Groups
• Censoring Unbiased Regression Trees and Ensembles
• Random forests

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