Revisiting optimal allocations for binary responses: insights from considering type-I error rate control.

This work revisits optimal response-adaptive designs from a type-I error rate perspective, highlighting when and how much these allocations exacerbate type-I error rate inflation-an issue previously undocumented. We explore a range of approaches from the literature that can be applied to reduce type-I error rate inflation. However, we found that all of these approaches fail to give a robust solution to the problem.

Randomization-Based Inference for MCP-Mod.

Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient's safety of a drug. The Generalized Multiple Comparison Procedures and Modeling approach is commonly used in Phase II trials for testing and estimation of dose-response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non existence of estimates.

Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials.

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling, defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies.