Personalised medicine with multiple treatments: a PhD thesis abstract  

作　　者：Zhilan Lou 

机构地区：[1]School of Statistics,East China Normal University,Shanghai,China

出　　处：《Statistical Theory and Related Fields》2017年第2期182-184,共3页统计理论及其应用（英文）

基　　金：The author would like to thank Jun Shao and Menggang Yu for their help with preparing the manuscript.This work was supported by the Chinese 111 Project[grant number B14019](for Lou and Shao).

摘　　要：When there is substantial heterogeneity of treatment effectiveness for comparative treatmentselection, it is crucial to identify individualised treatment rules for patients who have heterogeneous responses to treatment. Existing approaches include directly modelling clinical outcomeby defining the optimal treatment rule according to the interactions between treatment andcovariates and outcome weighted approach that uses clinical outcome as weights to maximise atarget function whose value directly reflects correct treatment assignment. All existing articles ofestimating individualised treatment rules are all assuming just two treatment assignments. Herewe propose an outcome weighted learning approach that uses a vector hinge loss to extend estimating individualised treatment rules in multi-category treatments case. The consistency of theresulting estimator is shown. We also demonstrate the performance of our approach in simulationstudies and a real data analysis.

关 键 词：Heterogeneity of treatment effectiveness individualised treatment rule risk bound RKHS weighted multi-category support vector machine 

分 类 号：O17[理学—数学]