Discussion of combining biomarkers to optimize patient treatment recommendations
- PMID: 24889265
- PMCID: PMC4248021
- DOI: 10.1111/biom.12189
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.
Keywords: Boosting; Outcome weighted learning; Personalized medicine; Support vector machine.
© 2014, The International Biometric Society.
Comment in
-
Rejoinder: Combining biomarkers to optimize patient treatment recommendations.Biometrics. 2014 Sep;70(3):719-20. doi: 10.1111/biom.12192. Epub 2014 May 30. Biometrics. 2014. PMID: 24889787 Free PMC article. No abstract available.
Comment on
-
Combining biomarkers to optimize patient treatment recommendations.Biometrics. 2014 Sep;70(3):695-707. doi: 10.1111/biom.12191. Epub 2014 May 30. Biometrics. 2014. PMID: 24889663 Free PMC article.
References
-
- Bartlett PL, Jordan MI, McAuliffe JD. Convexity, classification, and risk bounds. JASA. 2006;101:138–156.
-
- Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995:273–297.
-
- Culp M, Johnson K, Michailides G. ada: An r package for stochastic boosting. Journal of Statistical Software. 2006;17:9.
-
- Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A. Misc functions of the department of statistics (e1071), tu wien. R package. 2008:1–5.
-
- Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J. of Computer and System Sciences. 1997;55:119–139.
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
