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- W2770095711 abstract "When there is substantial heterogeneity of treatment effectiveness for comparative treatment selection, it is crucial to identify individualised treatment rules for patients who have heterogeneous responses to treatment. Existing approaches include directly modelling clinical outcome by defining the optimal treatment rule according to the interactions between treatment and covariates and outcome weighted approach that uses clinical outcome as weights to maximise a target function whose value directly reflects correct treatment assignment. All existing articles of estimating individualised treatment rules are all assuming just two treatment assignments. Here we 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 the resulting estimator is shown. We also demonstrate the performance of our approach in simulation studies and a real data analysis." @default.
- W2770095711 created "2017-12-04" @default.
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- W2770095711 date "2017-07-03" @default.
- W2770095711 modified "2023-10-18" @default.
- W2770095711 title "Personalised medicine with multiple treatments: a PhD thesis abstract" @default.
- W2770095711 cites W1720450983 @default.
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- W2770095711 doi "https://doi.org/10.1080/24754269.2017.1396426" @default.
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