Bayesian dose selection design for a binary outcome using restricted response adaptive randomization
Abstract Background In phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by...
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| Format: | Article |
| Language: | English |
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BMC
2017-09-01
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| Series: | Trials |
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| Online Access: | http://link.springer.com/article/10.1186/s13063-017-2004-6 |
| _version_ | 1811338545862803456 |
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| author | Caitlyn Meinzer Renee Martin Jose I. Suarez |
| author_facet | Caitlyn Meinzer Renee Martin Jose I. Suarez |
| author_sort | Caitlyn Meinzer |
| collection | DOAJ |
| description | Abstract Background In phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by exploring multiple doses through the use of adaptive strategies. However, the added flexibility may potentially increase the risk of making incorrect assumptions and reduce the total amount of information available across the dose range as a function of imbalanced sample size. Methods To balance these challenges, a novel placebo-controlled design is presented in which a restricted Bayesian response adaptive randomization (RAR) is used to allocate a majority of subjects to the optimal dose of active drug, defined as the dose with the lowest probability of poor outcome. However, the allocation between subjects who receive active drug or placebo is held constant to retain the maximum possible power for a hypothesis test of overall efficacy comparing the optimal dose to placebo. The design properties and optimization of the design are presented in the context of a phase II trial for subarachnoid hemorrhage. Results For a fixed total sample size, a trade-off exists between the ability to select the optimal dose and the probability of rejecting the null hypothesis. This relationship is modified by the allocation ratio between active and control subjects, the choice of RAR algorithm, and the number of subjects allocated to an initial fixed allocation period. While a responsive RAR algorithm improves the ability to select the correct dose, there is an increased risk of assigning more subjects to a worse arm as a function of ephemeral trends in the data. A subarachnoid treatment trial is used to illustrate how this design can be customized for specific objectives and available data. Conclusions Bayesian adaptive designs are a flexible approach to addressing multiple questions surrounding the optimal dose for treatment efficacy within the context of limited resources. While the design is general enough to apply to many situations, future work is needed to address interim analyses and the incorporation of models for dose response. |
| first_indexed | 2024-04-13T18:13:40Z |
| format | Article |
| id | doaj.art-c94dff75341b472aa18b4fdf1980e6ac |
| institution | Directory Open Access Journal |
| issn | 1745-6215 |
| language | English |
| last_indexed | 2024-04-13T18:13:40Z |
| publishDate | 2017-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Trials |
| spelling | doaj.art-c94dff75341b472aa18b4fdf1980e6ac2022-12-22T02:35:49ZengBMCTrials1745-62152017-09-0118111110.1186/s13063-017-2004-6Bayesian dose selection design for a binary outcome using restricted response adaptive randomizationCaitlyn Meinzer0Renee Martin1Jose I. Suarez2Data Coordination Unit, Department of Public Health Sciences, Medical University of South CarolinaData Coordination Unit, Department of Public Health Sciences, Medical University of South CarolinaDivision of Neurocritical Care, Departments of Anesthesiology and Critical Care Medicine, Neurology, and Neurosurgery, Johns Hopkins UniversityAbstract Background In phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by exploring multiple doses through the use of adaptive strategies. However, the added flexibility may potentially increase the risk of making incorrect assumptions and reduce the total amount of information available across the dose range as a function of imbalanced sample size. Methods To balance these challenges, a novel placebo-controlled design is presented in which a restricted Bayesian response adaptive randomization (RAR) is used to allocate a majority of subjects to the optimal dose of active drug, defined as the dose with the lowest probability of poor outcome. However, the allocation between subjects who receive active drug or placebo is held constant to retain the maximum possible power for a hypothesis test of overall efficacy comparing the optimal dose to placebo. The design properties and optimization of the design are presented in the context of a phase II trial for subarachnoid hemorrhage. Results For a fixed total sample size, a trade-off exists between the ability to select the optimal dose and the probability of rejecting the null hypothesis. This relationship is modified by the allocation ratio between active and control subjects, the choice of RAR algorithm, and the number of subjects allocated to an initial fixed allocation period. While a responsive RAR algorithm improves the ability to select the correct dose, there is an increased risk of assigning more subjects to a worse arm as a function of ephemeral trends in the data. A subarachnoid treatment trial is used to illustrate how this design can be customized for specific objectives and available data. Conclusions Bayesian adaptive designs are a flexible approach to addressing multiple questions surrounding the optimal dose for treatment efficacy within the context of limited resources. While the design is general enough to apply to many situations, future work is needed to address interim analyses and the incorporation of models for dose response.http://link.springer.com/article/10.1186/s13063-017-2004-6Dose selectionResponse adaptive randomizationPhase IIAdaptive designBayesian designClinical trial |
| spellingShingle | Caitlyn Meinzer Renee Martin Jose I. Suarez Bayesian dose selection design for a binary outcome using restricted response adaptive randomization Trials Dose selection Response adaptive randomization Phase II Adaptive design Bayesian design Clinical trial |
| title | Bayesian dose selection design for a binary outcome using restricted response adaptive randomization |
| title_full | Bayesian dose selection design for a binary outcome using restricted response adaptive randomization |
| title_fullStr | Bayesian dose selection design for a binary outcome using restricted response adaptive randomization |
| title_full_unstemmed | Bayesian dose selection design for a binary outcome using restricted response adaptive randomization |
| title_short | Bayesian dose selection design for a binary outcome using restricted response adaptive randomization |
| title_sort | bayesian dose selection design for a binary outcome using restricted response adaptive randomization |
| topic | Dose selection Response adaptive randomization Phase II Adaptive design Bayesian design Clinical trial |
| url | http://link.springer.com/article/10.1186/s13063-017-2004-6 |
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