Dynamic Treatment Regimes with Replicated Observations Available for Error-prone Covariates: a Q-learning Approach
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911829569044480 |
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| author | Liu, Dan He, Wenqing |
| author_facet | Liu, Dan He, Wenqing |
| contents | Dynamic treatment regimes (DTRs) have received an increasing interest in recent years. DTRs are sequences of treatment decision rules tailored to patient-level information. The main goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that yields the best expected clinical outcome. Q-learning has been considered as one of the most popular regression-based methods to estimate the optimal DTR. However, it is rarely studied in an error-prone setting, where the patient information is contaminated with measurement error. In this paper, we study the effect of covariate measurement error on Q-learning and propose a correction method to correct the measurement error in Q-learning. Simulation studies are conducted to assess the performance of the proposed method in Q-learning. We illustrate the use of the proposed method in an application to the sequenced treatment alternatives to relieve depression data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04696 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Treatment Regimes with Replicated Observations Available for Error-prone Covariates: a Q-learning Approach Liu, Dan He, Wenqing Methodology Dynamic treatment regimes (DTRs) have received an increasing interest in recent years. DTRs are sequences of treatment decision rules tailored to patient-level information. The main goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that yields the best expected clinical outcome. Q-learning has been considered as one of the most popular regression-based methods to estimate the optimal DTR. However, it is rarely studied in an error-prone setting, where the patient information is contaminated with measurement error. In this paper, we study the effect of covariate measurement error on Q-learning and propose a correction method to correct the measurement error in Q-learning. Simulation studies are conducted to assess the performance of the proposed method in Q-learning. We illustrate the use of the proposed method in an application to the sequenced treatment alternatives to relieve depression data. |
| title | Dynamic Treatment Regimes with Replicated Observations Available for Error-prone Covariates: a Q-learning Approach |
| topic | Methodology |
| url | https://arxiv.org/abs/2404.04696 |