C-learning: A new classification framework to estimate optimal dynamic treatment regimes

DOI10.1111/BIOM.12836zbMath1414.62485OpenAlexW2236852225WikidataQ47580192 ScholiaQ47580192MaRDI QIDQ3119933

Baqun Zhang, Min Zhang

Publication date: 13 March 2019

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://biostats.bepress.com/cgi/viewcontent.cgi?article=1122&context=umichbiostat

zbMATH Keywords

classificationCARTQ-learningaugmented inverse probability weighted estimatorprecision medicinedynamic treatment regimeA-learningsequential optimization problem

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Optimal stopping in statistics (62L15)

Related Items (6)

Subgroup identification and variable selection for treatment decision making ⋮ Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve With High-Dimensional Covariates ⋮ Optimal Treatment Regimes: A Review and Empirical Comparison ⋮ Generalization error bounds of dynamic treatment regimes in penalized regression-based learning ⋮ A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity ⋮ Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes

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