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

ABSTRACT: Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lasso); dimensionality reduction of the main effect matrix via linear combinations (PCA+lasso (where PCA is principal components analysis) or PLS+lasso (where PLS is partial least squares)); penalization of modified covariates or of the arm-specific biomarker effects (two-I model); gradient boosting; and univariate approach with control of multiple testing. We compared these methods via simulations, eval

SUBMITTER: Ternes N 

PROVIDER: S-EPMC5763402 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature