Revisiting Optimal Allocations for Binary Responses: Insights from Considering Type-I Error Rate Control

Music City Center 

In this talk, I revisit optimal response-adaptive designs through the lens of type-I error rate control, uncovering when and how these designs can inflate type-I error — an issue largely overlooked in earlier work. While several methods in the literature attempt to mitigate this inflation, we show they lack robustness, especially in finite samples.
To address this, I propose two new optimal allocation proportions that integrate a more reliable score test (instead of the Wald test) and finite-sample estimators. One allocation proportion optimizes statistical power; the other minimizes the number of treatment failures, both under a fixed variance constraint.

Simulations based on early-phase and confirmatory trials illustrate the practical benefits of these designs: improved patient outcomes and controlled type-I error. While our focus is on binary outcomes, the methodology naturally extends to other settings, including multi-arm trials and alternative performance metrics.

The talk will provide both theoretical insights and practical guidance for designing robust adaptive trials.

Neyman allocation

Patient benefit

RSHIR allocation

Score test

Wald test