Adaptive Enrichment Designs for Stroke Clinical Trials
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Most randomized trials are designed to determine average treatment effects for a target population. This is ideal if the treatment has an identical effect on all participants, but can be problematic if there are important differences in benefits and harms for subpopulations. For example, standard trial designs are not targeted to determine whether a treatment benefits most patients, benefits only a select few, or benefits some patients and harms others. The impact is that treatment recommendations based on the results of standard trial designs may be suboptimal, leading to poor patient outcomes and wasting healthcare resources.
Randomized trial designs that adaptively change enrollment criteria during a trial, called adaptive enrichment designs,1 have potential to provide improved information about which subpopulations benefit from new treatments. These designs may be useful when a subpopulation is suspected to be more likely to benefit from the experimental treatment than the rest of the target population. The subpopulation could be defined, for example, by a biomarker or risk score measured at baseline. Adaptive enrichment designs have the capability of restricting enrollment to such a subpopulation if early data indicate that the complementary population is not benefiting.
There is interest from the Patient-Centered Outcomes Research Institute in adaptive designs. According to the Patient-Centered Outcomes Research Institute Methodology Report, “adaptive designs are particularly appealing for PCOR (Patient-Centered Outcomes Research) because they could maintain many of the advantages of randomized clinical trials while minimizing some of the disadvantages.”2
Adaptive enrichment designs have potential to improve power for detecting subpopulation treatment effects. Although there is much potential for producing stronger evidence about which subpopulations benefit by using adaptive designs, these designs are no panacea. Indiscriminate application of adaptive designs could be detrimental. This is because these designs require a larger sample size than a standard design enrolling …