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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2304.05658 |
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| _version_ | 1866910835085934592 |
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| author | Bornkamp, Björn Zaoli, Silvia Azzarito, Michela Martin, Ruvie Müller, Carsten Philipp Moloney, Conor Capestro, Giulia Ohlssen, David Baillie, Mark |
| author_facet | Bornkamp, Björn Zaoli, Silvia Azzarito, Michela Martin, Ruvie Müller, Carsten Philipp Moloney, Conor Capestro, Giulia Ohlssen, David Baillie, Mark |
| contents | We present the motivation, experience and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organisation. We outline the motivation for running the challenge, the challenge rules and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings, and how these learnings can be translated into statistical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05658 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation Bornkamp, Björn Zaoli, Silvia Azzarito, Michela Martin, Ruvie Müller, Carsten Philipp Moloney, Conor Capestro, Giulia Ohlssen, David Baillie, Mark Applications We present the motivation, experience and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organisation. We outline the motivation for running the challenge, the challenge rules and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings, and how these learnings can be translated into statistical practice. |
| title | Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation |
| topic | Applications |
| url | https://arxiv.org/abs/2304.05658 |