Adaptive Experiment Design with Synthetic Controls
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911774064771072 |
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| author | Hüyük, Alihan Qian, Zhaozhi van der Schaar, Mihaela |
| author_facet | Hüyük, Alihan Qian, Zhaozhi van der Schaar, Mihaela |
| contents | Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations - especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Experiment Design with Synthetic Controls Hüyük, Alihan Qian, Zhaozhi van der Schaar, Mihaela Machine Learning Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations - especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments. |
| title | Adaptive Experiment Design with Synthetic Controls |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.17205 |