Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.

Abstract

Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox… (More)
DOI: 10.1002/sim.6927

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@article{Terns2016EmpiricalEO, title={Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.}, author={Nils Tern{\`e}s and Federico Rotolo and Stefan Michiels}, journal={Statistics in medicine}, year={2016}, volume={35 15}, pages={2561-73} }