Journal
STROKE
Volume 47, Issue 12, Pages 2931-2937Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/STROKEAHA.116.015436
Keywords
adaptive trial design; endovascular techniques; stroke
Categories
Funding
- National Institutes of Health [UL1 TR001085, R01 NS075209, U10 NS086487, U01 NS 087748]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1407828] Funding Source: National Science Foundation
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Background and Purpose-Adaptive trial designs that allow enrichment of the study population through subgroup selection can increase the chance of a positive trial when there is a differential treatment effect among patient subgroups. The goal of this study is to illustrate the potential benefit of adaptive subgroup selection in endovascular stroke studies. Methods-We simulated the performance of a trial design with adaptive subgroup selection and compared it with that of a traditional design. Outcome data were based on 90-day modified Rankin Scale scores, observed in IMS III (Interventional Management of Stroke III), among patients with a vessel occlusion on baseline computed tomographic angiography (n= 382). Patients were categorized based on 2 methods: (1) according to location of the arterial occlusive lesion and onset-to-randomization time and (2) according to onset-to-randomization time alone. The power to demonstrate a treatment benefit was based on 10 000 trial simulations for each design. Results-The treatment effect was relatively homogeneous across categories when patients were categorized based on arterial occlusive lesion and time. Consequently, the adaptive design had similar power (47%) compared with the fixed trial design (45%). There was a differential treatment effect when patients were categorized based on time alone, resulting in greater power with the adaptive design (82%) than with the fixed design (57%). Conclusions-These simulations, based on real-world patient data, indicate that adaptive subgroup selection has merit in endovascular stroke trials as it substantially increases power when the treatment effect differs among subgroups in a predicted pattern.
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