Hybrid continuous reassessment method with overdose control for safer dose escalation
Phase 1 oncology studies focus on safety of novel treatments and identifying a dose associated with acceptable toxicity level. Various model-based designs have been proposed for guiding dose escalation and estimating maximum tolerated dose in dose-finding studies. However, these methods are either excessively conservative or imprudent by allowing overly toxic doses. Transparent and easy to implement model-assisted designs have also received increasing attention but require pre-set rules including perceived dose levels. Therefore, we propose a hybrid model-based design that has a high probability to select MTD with a good balance of overdose control by disentangling in two separate models, which is flexible and easy to implement. Extensive simulations show the model to have real promise.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
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| Enthalten in: |
Journal of biopharmaceutical statistics - 33(2023), 5 vom: 03. Sept., Seite 586-595 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ghosh, Debopriya [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 22.08.2023 Date Revised 29.08.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/10543406.2023.2170401 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM352258918 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Phase 1 oncology studies focus on safety of novel treatments and identifying a dose associated with acceptable toxicity level. Various model-based designs have been proposed for guiding dose escalation and estimating maximum tolerated dose in dose-finding studies. However, these methods are either excessively conservative or imprudent by allowing overly toxic doses. Transparent and easy to implement model-assisted designs have also received increasing attention but require pre-set rules including perceived dose levels. Therefore, we propose a hybrid model-based design that has a high probability to select MTD with a good balance of overdose control by disentangling in two separate models, which is flexible and easy to implement. Extensive simulations show the model to have real promise | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Dose-escalation | |
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| 700 | 1 | |a Xie, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Liangcai |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Mohanty, Surya |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiang |e verfasserin |4 aut | |
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