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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00864 |
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| _version_ | 1866911790657437696 |
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| author | Xu, Qi Cao, Xiaoke Chen, Geping Zeng, Hanqi Fu, Haoda Qu, Annie |
| author_facet | Xu, Qi Cao, Xiaoke Chen, Geping Zeng, Hanqi Fu, Haoda Qu, Annie |
| contents | Individualized treatment rules (ITRs) have been widely applied in many fields such as precision medicine and personalized marketing. Beyond the extensive studies on ITR for binary or multiple treatments, there is considerable interest in applying combination treatments. This paper introduces a novel ITR estimation method for combination treatments incorporating interaction effects among treatments. Specifically, we propose the generalized $ψ$-loss as a non-convex surrogate in the residual weighted learning framework, offering desirable statistical and computational properties. Statistically, the minimizer of the proposed surrogate loss is Fisher-consistent with the optimal decision rules, incorporating interaction effects at any intensity level - a significant improvement over existing methods. Computationally, the proposed method applies the difference-of-convex algorithm for efficient computation. Through simulation studies and real-world data applications, we demonstrate the superior performance of the proposed method in recommending combination treatments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00864 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-Label Residual Weighted Learning for Individualized Combination Treatment Rule Xu, Qi Cao, Xiaoke Chen, Geping Zeng, Hanqi Fu, Haoda Qu, Annie Methodology Individualized treatment rules (ITRs) have been widely applied in many fields such as precision medicine and personalized marketing. Beyond the extensive studies on ITR for binary or multiple treatments, there is considerable interest in applying combination treatments. This paper introduces a novel ITR estimation method for combination treatments incorporating interaction effects among treatments. Specifically, we propose the generalized $ψ$-loss as a non-convex surrogate in the residual weighted learning framework, offering desirable statistical and computational properties. Statistically, the minimizer of the proposed surrogate loss is Fisher-consistent with the optimal decision rules, incorporating interaction effects at any intensity level - a significant improvement over existing methods. Computationally, the proposed method applies the difference-of-convex algorithm for efficient computation. Through simulation studies and real-world data applications, we demonstrate the superior performance of the proposed method in recommending combination treatments. |
| title | Multi-Label Residual Weighted Learning for Individualized Combination Treatment Rule |
| topic | Methodology |
| url | https://arxiv.org/abs/2310.00864 |