5 papers
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…
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, SofÃa 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…
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…