Discussion of combining biomarkers to optimize patient treatment recommendations

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.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  M. Kosorok,et al.  Reinforcement learning design for cancer clinical trials , 2009, Statistics in medicine.

[3]  Yoav Freund,et al.  Boosting: Foundations and Algorithms , 2012 .

[4]  Peter Buhlmann,et al.  BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING , 2007, 0804.2752.

[5]  Donglin Zeng,et al.  Estimating Individualized Treatment Rules Using Outcome Weighted Learning , 2012, Journal of the American Statistical Association.

[6]  Michael I. Jordan,et al.  Convexity, Classification, and Risk Bounds , 2006 .

[7]  Holly Janes,et al.  Combining biomarkers to optimize patient treatment recommendations , 2014, Biometrics.

[8]  Eric B. Laber,et al.  A Robust Method for Estimating Optimal Treatment Regimes , 2012, Biometrics.

[10]  S. Murphy,et al.  Optimal dynamic treatment regimes , 2003 .

[11]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[12]  H. Zou,et al.  NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES. , 2008, The annals of applied statistics.

[13]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[14]  Min Zhang,et al.  Estimating optimal treatment regimes from a classification perspective , 2012, Stat.

[15]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[16]  Mark Culp,et al.  and Development , 1998 .

[17]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[18]  Tong Zhang.  Statistical behavior and consistency of classification methods based on convex risk minimization , 2003 .

[19]  M. J. Laan,et al.  Optimal Dynamic Treatments in Resource-Limited Settings , 2015 .

[20]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT '99.

[21]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[22]  S. Murphy,et al.  PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES. , 2011, Annals of statistics.