Zhao Ying-Qi, Kosorok Michael R
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin 53792, U.S.A.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Biometrics. 2014 Sep;70(3):713-6. doi: 10.1111/biom.12189. Epub 2014 May 30.
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.
康、杰恩斯和黄提出了一种有趣的增强方法，用于组合生物标志物以进行治疗选择。该方法需要使用标志物对治疗效果进行建模。我们讨论了另一种方法，即结果加权学习。这种方法避免了对结果进行建模的需要，因此对于模型错误设定可能更具鲁棒性。
