Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection

DOI10.1016/j.csda.2021.107167OpenAlexW3119531432MaRDI QIDQ830063

Kwang Woo Ahn, Wael Saber, Mi-Ok Kim, Yizeng He, Soyoung Kim

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2021.107167

zbMATH Keywords

competing riskspersonalized medicineoptimal treatment regimebi-level variable selection

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Significance test for semiparametric conditional average treatment effects and other structural functions ⋮ Impact of covariate omission and categorization from the Fine-Gray model in randomized-controlled trials

Uses Software

• cmprsk
• Rglpk

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Group and within-group variable selection for competing risks data
• Doubly regularized Cox regression for high-dimensional survival data with group structures
• Bayesian inference for causal effects: The role of randomization
• Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective
• High-dimensional \(A\)-learning for optimal dynamic treatment regimes
• Doubly robust estimation of optimal treatment regimes for survival data -- with application to an HIV/AIDS study
• A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data
• Practical methods for competing risks data: A review
• Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues
• A group bridge approach for variable selection
• A nonidentifiability aspect of the problem of competing risks.
• Generalised linear models for correlated pseudo-observations, with applications to multi-state models
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• A comparison of model selection methods for prediction in the presence of multiply imputed data
• Multiply Robust Estimation in Regression Analysis With Missing Data
• Doubly robust learning for estimating individualized treatment with censored data
• Estimation with missing data: beyond double robustness
• A selective review of group selection in high-dimensional models

This page was built for publication: Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection