Optimal interim decision rules based on a binary surrogate outcome for adaptive biomarker-based trials in oncology
Adaptive enrichment designs represent a promising approach to evaluate targeted therapies, for example, in oncology. They allow selection of the most promising target population in an interim analysis and then combination of the data from the two trial stages for the final proof of efficacy. Applica...
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| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
2017
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| In: |
Statistics in biopharmaceutical research
Year: 2017, Volume: 9, Issue: 4, Pages: 321-332 |
| ISSN: | 1946-6315 |
| DOI: | 10.1080/19466315.2017.1323670 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1080/19466315.2017.1323670 Verlag, Volltext: https://doi.org/10.1080/19466315.2017.1323670 
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| Author Notes: | Johannes Krisam and Meinhard Kieser |
MARC
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| 520 | |a Adaptive enrichment designs represent a promising approach to evaluate targeted therapies, for example, in oncology. They allow selection of the most promising target population in an interim analysis and then combination of the data from the two trial stages for the final proof of efficacy. Application of these designs is motivated by the assumption that there might be a biomarker-defined subgroup of patients with an increased treatment benefit as compared to the total patient population. If the primary outcome is a time-to-event variable and the respective event takes a relatively long time to be observed, it could be beneficial to select the most promising patient population based on an earlier available binary surrogate, for example, response, to save time and costs. We propose an adaptive enrichment design which allows us to implement such a trial setting. For this design, optimal decision rules are derived minimizing the expected loss incurred due to a false interim decision. These rules are compared to ad hoc rules in terms of selection probability and power within a simulation study which is motivated by a clinical trial example. Furthermore, the impact of the correlation between surrogate and primary outcome on power is investigated. Supplementary materials for this article are available online. | ||
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| 650 | 4 | |a Predictive biomarker | |
| 650 | 4 | |a Targeted therapies | |
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