Combining biomarkers to optimize patient treatment recommendations
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Publication:2927626
DOI10.1111/biom.12191zbMath1299.62125OpenAlexW1750038386WikidataQ34588910 ScholiaQ34588910MaRDI QIDQ2927626
Chaeryon Kang, Unnamed Author, Ying Huang
Publication date: 4 November 2014
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc4248022
Related Items (14)
Greedy outcome weighted tree learning of optimal personalized treatment rules ⋮ Multiple Domain and Multiple Kernel Outcome-Weighted Learning for Estimating Individualized Treatment Regimes ⋮ Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve With High-Dimensional Covariates ⋮ Pool adjacent violators algorithm–assisted learning with application on estimating optimal individualized treatment regimes ⋮ Functional additive models for optimizing individualized treatment rules ⋮ Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data ⋮ A constrained single‐index regression for estimating interactions between a treatment and covariates ⋮ Variable selection for estimating the optimal treatment regimes in the presence of a large number of covariates ⋮ Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records ⋮ Bayesian predictive modeling for genomic based personalized treatment selection ⋮ Doubly-robust dynamic treatment regimen estimation via weighted least squares ⋮ Hypervolume under ROC manifold for discrete biomarkers with ties ⋮ Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree ⋮ Interpretable Dynamic Treatment Regimes
Uses Software
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