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

Statistics in Medicine

Volumn 35, Issue 15, 2016, Pages 2561-2573

  Ternès, Nils ^a,b   Rotolo, Federico ^a,b   Michiels, Stefan ^a,b  

^a LABORATOIRE DES SCIENCES DU CLIMAT ET DE L ENVIRONNEMENT   (France)

^b INSTITUT GUSTAVE ROUSSY   (France)

Author keywords

Cox model; False discovery rate; Lasso penalty; Prognostic biomarkers; Variable selection

Indexed keywords

BIOLOGICAL MARKER; TRANSCRIPTOME;

AKAIKE INFORMATION CRITERION; ANALYTICAL ERROR; ARTICLE; BAYES THEOREM; BAYESIAN INFORMATION CRITERION; BREAST CANCER; CANCER PATIENT; CANCER PROGNOSIS; FALSE DISCOVERY RATE; FALSE NEGATIVE RESULT; GENE EXPRESSION; HANNAN AND QUINN INFORMATION CRITERION; MATHEMATICAL COMPUTING; PRACTICE GUIDELINE; PROPORTIONAL HAZARDS MODEL; RISK INFORMATION CRITERION; SIMULATION; STATISTICAL CONCEPTS; STATISTICAL PARAMETERS; VALIDATION STUDY; BREAST TUMOR; GENETICS; HUMAN;

BAYES THEOREM; BIOMARKERS; BREAST NEOPLASMS; HUMANS; PROPORTIONAL HAZARDS MODELS; TRANSCRIPTOME;

EID: 84960539558     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6927     Document Type: Article

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