title: Discussion of combining biomarkers to optimize patient treatment recommendations.
abstract: Kang, Janes and Huang propose an interesting boosting method to combine biomarkers for treatment selection. The method requires modeling the treatment effects using markers. We discuss an alternative method, outcome weighted learning. This method sidesteps the need for modeling the outcomes, and thus can be more robust to model misspecification.
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin 53792, U.S.A.
authors.forename: Ying-Qi
authors.initials: YQ
authors.lastname: Zhao
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.|Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok
mesh_terms: D014408:Biomarkers, Tumor / Q000097:blood*; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000097:blood* / Q000628:therapy*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor
keywords: Boosting; Outcome weighted learning; Personalized medicine; Support vector machine