5 papers · 1 filter
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…
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…
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…
An integer programming-based approach to construct exact two-sample binomial tests with maximum power
Stef Baas, Yaron Racah, Elad Berkman +1
Comparing binomial proportions is a foundational task in clinical trials; however, practitioners often use a liberal likelihood-based test or the conservative Fisher's exact test.…