Personalised medicine with multiple treatments: a PhD thesis 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...
| Published in: | Statistical Theory and Related Fields |
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| Main Author: | |
| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-07-01
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2017.1396426 |
| Summary: | 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. |
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| ISSN: | 2475-4269 2475-4277 |