Identification of biomarker‐by‐treatment interactions in randomized clinical trials with survival outcomes and high‐dimensional spaces

DOI10.1002/bimj.201500234zbMath1369.62306OpenAlexW2554645567WikidataQ39175273 ScholiaQ39175273MaRDI QIDQ5280185

Georg Heinze, Nils Ternès, Stefan Michiels, Federico Rotolo

Publication date: 20 July 2017

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201500234

zbMATH Keywords

survivalvariable selectionmultiple testinghigh-dimensionalprecision medicinestratified medicinebiomarker-by-treatment interactions

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Testing in survival analysis and censored data (62N03) Paired and multiple comparisons; multiple testing (62J15)

Related Items (5)

A Scalable Hierarchical Lasso for Gene–Environment Interactions ⋮ IPF-LASSO: integrative \(L_1\)-penalized regression with penalty factors for prediction based on multi-omics data ⋮ Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials ⋮ Sparse classification with paired covariates ⋮ Oblique random survival forests

Uses Software

• mboost
• glmnet
• grplasso
• corpor
• hierNet
• corpcor
• plsRcox

Cites Work

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• A lasso for hierarchical interactions
• A significance test for the lasso
• Boosting With theL₂Loss
• Operating Characteristics and Extensions of the False Discovery Rate Procedure
• A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Adaptive Lasso for Cox's proportional hazards model
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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