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Bayesian Two-Stage Adaptive Design in Bioequivalence
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| Zeitschriftentitel: | The International Journal of Biostatistics |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | The International Journal of Biostatistics, 16, 2020, 1 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Walter de Gruyter GmbH
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| Schlagwörter: |
| author_facet |
Liu, Shengjie Gao, Jun Zheng, Yuling Huang, Lei Yan, Fangrong Liu, Shengjie Gao, Jun Zheng, Yuling Huang, Lei Yan, Fangrong |
|---|---|
| author |
Liu, Shengjie Gao, Jun Zheng, Yuling Huang, Lei Yan, Fangrong |
| spellingShingle |
Liu, Shengjie Gao, Jun Zheng, Yuling Huang, Lei Yan, Fangrong The International Journal of Biostatistics Bayesian Two-Stage Adaptive Design in Bioequivalence Statistics, Probability and Uncertainty General Medicine Statistics and Probability |
| author_sort |
liu, shengjie |
| spelling |
Liu, Shengjie Gao, Jun Zheng, Yuling Huang, Lei Yan, Fangrong 1557-4679 2194-573X Walter de Gruyter GmbH Statistics, Probability and Uncertainty General Medicine Statistics and Probability http://dx.doi.org/10.1515/ijb-2018-0105 <jats:title>Abstract</jats:title> <jats:p>Bioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C) in the following aspects. First, it not only incorporates historical information and expert information, but further combines experimental data flexibly to aid decision-making. Secondly, its sample re-estimation strategy is based on the ratio of the information in interim analysis to total information, which is simpler in calculation than the Potvin’s method. Simulation results manifested that the two-stage design can be combined with various stop boundary functions, and the results are different. Moreover, the proposed method saves sample size compared to the Potvin’s method under the conditions that type I error rate is below 0.05 and statistical power reaches 80 %.</jats:p> Bayesian Two-Stage Adaptive Design in Bioequivalence The International Journal of Biostatistics |
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| title |
Bayesian Two-Stage Adaptive Design in Bioequivalence |
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Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_full |
Bayesian Two-Stage Adaptive Design in Bioequivalence |
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Bayesian Two-Stage Adaptive Design in Bioequivalence |
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Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_short |
Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_sort |
bayesian two-stage adaptive design in bioequivalence |
| topic |
Statistics, Probability and Uncertainty General Medicine Statistics and Probability |
| url |
http://dx.doi.org/10.1515/ijb-2018-0105 |
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2020 |
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<jats:title>Abstract</jats:title>
<jats:p>Bioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C) in the following aspects. First, it not only incorporates historical information and expert information, but further combines experimental data flexibly to aid decision-making. Secondly, its sample re-estimation strategy is based on the ratio of the information in interim analysis to total information, which is simpler in calculation than the Potvin’s method. Simulation results manifested that the two-stage design can be combined with various stop boundary functions, and the results are different. Moreover, the proposed method saves sample size compared to the Potvin’s method under the conditions that type I error rate is below 0.05 and statistical power reaches 80 %.</jats:p> |
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| author | Liu, Shengjie, Gao, Jun, Zheng, Yuling, Huang, Lei, Yan, Fangrong |
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| description | <jats:title>Abstract</jats:title> <jats:p>Bioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C) in the following aspects. First, it not only incorporates historical information and expert information, but further combines experimental data flexibly to aid decision-making. Secondly, its sample re-estimation strategy is based on the ratio of the information in interim analysis to total information, which is simpler in calculation than the Potvin’s method. Simulation results manifested that the two-stage design can be combined with various stop boundary functions, and the results are different. Moreover, the proposed method saves sample size compared to the Potvin’s method under the conditions that type I error rate is below 0.05 and statistical power reaches 80 %.</jats:p> |
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| spelling | Liu, Shengjie Gao, Jun Zheng, Yuling Huang, Lei Yan, Fangrong 1557-4679 2194-573X Walter de Gruyter GmbH Statistics, Probability and Uncertainty General Medicine Statistics and Probability http://dx.doi.org/10.1515/ijb-2018-0105 <jats:title>Abstract</jats:title> <jats:p>Bioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C) in the following aspects. First, it not only incorporates historical information and expert information, but further combines experimental data flexibly to aid decision-making. Secondly, its sample re-estimation strategy is based on the ratio of the information in interim analysis to total information, which is simpler in calculation than the Potvin’s method. Simulation results manifested that the two-stage design can be combined with various stop boundary functions, and the results are different. Moreover, the proposed method saves sample size compared to the Potvin’s method under the conditions that type I error rate is below 0.05 and statistical power reaches 80 %.</jats:p> Bayesian Two-Stage Adaptive Design in Bioequivalence The International Journal of Biostatistics |
| spellingShingle | Liu, Shengjie, Gao, Jun, Zheng, Yuling, Huang, Lei, Yan, Fangrong, The International Journal of Biostatistics, Bayesian Two-Stage Adaptive Design in Bioequivalence, Statistics, Probability and Uncertainty, General Medicine, Statistics and Probability |
| title | Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_full | Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_fullStr | Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_full_unstemmed | Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_short | Bayesian Two-Stage Adaptive Design in Bioequivalence |
| title_sort | bayesian two-stage adaptive design in bioequivalence |
| title_unstemmed | Bayesian Two-Stage Adaptive Design in Bioequivalence |
| topic | Statistics, Probability and Uncertainty, General Medicine, Statistics and Probability |
| url | http://dx.doi.org/10.1515/ijb-2018-0105 |