Data-driven controlled subgroup selection in clinical trials
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908800861077504 |
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| author | Müller, Manuel M. Bornkamp, Björn Bretz, Frank Cannings, Timothy I. Liu, Wei Reeve, Henry W. J. Samworth, Richard J. Sfikas, Nikolaos Wan, Fang Sechidis, Konstantinos |
| author_facet | Müller, Manuel M. Bornkamp, Björn Bretz, Frank Cannings, Timothy I. Liu, Wei Reeve, Henry W. J. Samworth, Richard J. Sfikas, Nikolaos Wan, Fang Sechidis, Konstantinos |
| contents | Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post-selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data-driven subgroup identification. In this paper, we present two methods for subgroup selection in regression problems: one based on generalised linear modelling and another on isotonic regression. We demonstrate how these methods can be used for data-driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modelling assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven controlled subgroup selection in clinical trials Müller, Manuel M. Bornkamp, Björn Bretz, Frank Cannings, Timothy I. Liu, Wei Reeve, Henry W. J. Samworth, Richard J. Sfikas, Nikolaos Wan, Fang Sechidis, Konstantinos Methodology Applications Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post-selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data-driven subgroup identification. In this paper, we present two methods for subgroup selection in regression problems: one based on generalised linear modelling and another on isotonic regression. We demonstrate how these methods can be used for data-driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modelling assumptions. |
| title | Data-driven controlled subgroup selection in clinical trials |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2512.15676 |