This workshop focuses on adaptive enrichment designs, that is, designs with preplanned rules for modifying enrollment criteria based on data accrued in an ongoing trial. For example, enrollment of a subpopulation where there is sufficient evidence of treatment efficacy, futility, or harm could be stopped, while enrollment for the complementary subpopulation is continued. Such designs may be useful when it’s suspected that a subpopulation may benefit more than the overall population. The subpopulation could be defined by a risk score or biomarker measured at baseline. Adaptive enrichment designs have potential to provide stronger evidence than standard designs about treatment benefits for the subpopulation, its complement, and the combined population. However, there are tradeoffs in using such designs, which typically require greater sample size than designs that focus only on the combined population.

We present new statistical methods for adaptive enrichment designs (part 1 of the course), simulation-based case studies in Stroke, Cardiac Resynchronization Therapy, Alzheimer’s Disease, and HIV (part 2 of the course), and trial optimization software (part 3 of the course). The tradeoffs involved in using adaptive enrichment designs, compared to standard designs, will be presented. Our software searches over hundreds of candidate adaptive designs with the aim of finding one that satisfies the user’s requirements for power and Type I error at the minimum sample size, which is then compared to simpler designs in terms of sample size, duration, power, type I error, and bias in an automatically generated report.

Participants should be familiar with the following concepts: type I error, power, bias, variance, and confidence intervals.