8 papers
Adaptive clinical trials based on design-optimal e-values with automatic curtailment: An application to single-arm trials with binary data
Stef Baas, Judith ter Schure, Joost van Rosmalen
The e-value is gaining traction as a robust alternative to p-values and Bayes factors for quantifying statistical evidence. e-values are a promising method for adaptive clinical tr…
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.…
Thompson, Ulam, or Gauss? Multi-criteria recommendations for posterior probability computation methods in Bayesian response-adaptive trials
Daniel Kaddaj, Stef Baas, Edwin Y. N. Tang +3
Bayesian adaptive designs enable flexible clinical trials by adapting features based on accumulating data. Among these, Bayesian Response-Adaptive Randomization (BRAR) skews patien…
Exact statistical analysis for response-adaptive clinical trials: A general and computationally tractable approach
Stef Baas, Peter Jacko, Sofía S. Villar
Response-adaptive clinical trial designs allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. Response-adaptive des…