Power of an Adaptive Trial Design for Endovascular Stroke Studies
Author(s) -
Maarten G. Lansberg,
Ninad S. Bhat,
Sharon D. Yeatts,
Yuko Y. Palesch,
Joseph P. Broderick,
Gregory W. Albers,
Tze Leung Lai,
Philip W. Lavori
Publication year - 2016
Publication title -
stroke
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.397
H-Index - 319
eISSN - 1524-4628
pISSN - 0039-2499
DOI - 10.1161/strokeaha.116.015436
Subject(s) - medicine , modified rankin scale , randomization , stroke (engine) , subgroup analysis , clinical trial , stroke recovery , population , randomized controlled trial , differential treatment , selection (genetic algorithm) , physical therapy , surgery , ischemic stroke , meta analysis , artificial intelligence , mechanical engineering , environmental health , ischemia , rehabilitation , computer science , engineering , international trade , business
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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