An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests

DOI10.1080/10618600.2018.1451337OpenAlexW2794999241WikidataQ96304064 ScholiaQ96304064MaRDI QIDQ3391166

Hua Zhou, Jin Zhou, Haoda Fu, Kevin Doubleday

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://europepmc.org/articles/pmc7286561

zbMATH Keywords

optimizationvalue functionrecursive partitioningvariable importanceprecision medicinesubgroup identification

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

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Uses Software

• R
• randomForest

Cites Work

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• Bagging predictors
• Performance guarantees for individualized treatment rules
• Support-vector networks
• Regularized outcome weighted subgroup identification for differential treatment effects
• Using decision lists to construct interpretable and parsimonious treatment regimes
• Evaluating marker-guided treatment selection strategies
• Analysis of randomized comparative clinical trial data for personalized treatment selections
• Tree-based methods for individualized treatment regimes
• The central role of the propensity score in observational studies for causal effects
• Estimating Individualized Treatment Rules Using Outcome Weighted Learning
• A Robust Method for Estimating Optimal Treatment Regimes
• Effectively Selecting a Target Population for a Future Comparative Study
• Problems in the Analysis of Survey Data, and a Proposal
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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