Nonlinear predictive directions in clinical trials

DOI10.1016/j.csda.2022.107476OpenAlexW4220870058MaRDI QIDQ2157508

Young Joo Cho, Debashis Ghosh, Xiang Zhan

Publication date: 22 July 2022

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

Full work available at URL: https://doi.org/10.1016/j.csda.2022.107476

zbMATH Keywords

kernel methodsmachine learningcausal effectpersonalized medicineheterogeneity of treatment effect

Mathematics Subject Classification ID

Statistics (62-XX)

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Cites Work

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• Estimating treatment effect heterogeneity in randomized program evaluation
• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• Propensity score modelling in observational studies using dimension reduction methods
• Responder identification in clinical trials with censored data
• Dimension reduction for conditional mean in regression
• A general theory for nonlinear sufficient dimension reduction: formulation and estimation
• Kernel sliced inverse regression: regularization and consistency
• Kernel dimension reduction in regression
• Learning theory estimates via integral operators and their approximations
• 10.1162/153244302760185252
• Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction
• Analysis of randomized comparative clinical trial data for personalized treatment selections
• The central role of the propensity score in observational studies for causal effects
• Statistics and Causal Inference
• Sliced Inverse Regression for Dimension Reduction
• Radial Basis Functions
• Functional sliced inverse regression analysis
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Quantile-Optimal Treatment Regimes
• Stability Selection
• An Adaptive Estimation of Dimension Reduction Space
• Testing for Qualitative Interactions between Treatment Effects and Patient Subsets
• Flexible Imputation of Missing Data, Second Edition
• Identifying predictive markers for personalized treatment selection
• On estimating regression-based causal effects using sufficient dimension reduction
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models
• Theory of Reproducing Kernels
• Comment
• Random forests

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