9 papers
Informed Burn-In Decisions in RAR: Harmonizing Adaptivity and Inferential Precision Based on Study Setting
Lukas Pin, Stef Baas, Gianmarco Caruso +2
Response-Adaptive Randomization (RAR) is recognized for its potential to deliver improvements in patient benefit. However, the utility of RAR is contingent on regularization method…
Bayesian Optimal Phase II design with optimised stopping boundaries and response-adaptive randomisation
Connor Fitchett, Ayon Mukherjee, Sofía S. Villar +1
The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisati…
A computational method for type I error rate control in power-maximizing response-adaptive randomization
Stef Baas, Lukas Pin, Sofía S. Villar +1
Maximizing statistical power in experimental design often involves imbalanced treatment allocation, but several challenges hinder its practical adoption: (1) the misconception that…
Is 1:1 Always Most Powerful? Why Careful Determination of Allocation Ratios Matters in Trial Design
Lukas Pin, Stef Baas, David S. Robertson +1
The principle of allocating an equal number of patients to each arm in a randomized controlled trial remains widely believed to be optimal for maximising statistical power. However…
Safety-Driven Response Adaptive Randomisation: An Application in Non-inferiority Oncology Trials
Maria Vittoria Chiaruttini, Lukas Pin, Sofia S. Villar
The majority of response-adaptive randomisation (RAR) designs in the literature rely on efficacy data to guide dynamic patient allocation. However, their applicability becomes limi…
A burn-in(g) question: How long should an initial equal randomization stage be before Bayesian response-adaptive randomization?
Edwin Y. N. Tang, Stef Baas, Daniel Kaddaj +3
Response-adaptive randomization (RAR) can increase participant benefit in clinical trials, but also complicates statistical analysis. The burn-in period (a non-adaptive initial sta…