A class of computational methods to reduce selection bias when designing Phase 3 clinical trials
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Wiley Open Access
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| Format: | Artículo Open Access |
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2024
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| _version_ | 1867008852759674880 |
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| author | Tianyu Zhan |
| author_facet | Tianyu Zhan Tianyu Zhan |
| collection | Wiley Open Access |
| contents | A class of computational methods to reduce selection bias when designing Phase 3 clinical trials Tianyu Zhan Statistics in Medicine When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retrospective research articles reported the phenomenon of “diminished treatment effect in Phase 3” based on many case studies. Even under basic assumptions, it was shown that the commonly used estimator could substantially overestimate the efficacy of selected group(s). As alternatives, we propose a class of computational methods to reduce estimation bias and mean squared error with a broader scope of multiple treatment groups and flexibility to accommodate summary results by group as input. Based on simulation studies and a real data example, we provide practical implementation guidance for this class of methods under different scenarios. For more complicated problems, our framework can serve as a starting point with additional layers built in. Proposed methods can also be widely applied to other selection problems. 10.1002/sim.10041 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.10041 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10041 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | A class of computational methods to reduce selection bias when designing Phase 3 clinical trials Tianyu Zhan Statistics in Medicine A class of computational methods to reduce selection bias when designing Phase 3 clinical trials Tianyu Zhan Statistics in Medicine When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retrospective research articles reported the phenomenon of “diminished treatment effect in Phase 3” based on many case studies. Even under basic assumptions, it was shown that the commonly used estimator could substantially overestimate the efficacy of selected group(s). As alternatives, we propose a class of computational methods to reduce estimation bias and mean squared error with a broader scope of multiple treatment groups and flexibility to accommodate summary results by group as input. Based on simulation studies and a real data example, we provide practical implementation guidance for this class of methods under different scenarios. For more complicated problems, our framework can serve as a starting point with additional layers built in. Proposed methods can also be widely applied to other selection problems. 10.1002/sim.10041 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | A class of computational methods to reduce selection bias when designing Phase 3 clinical trials |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10041 |