4 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…
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…
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…