Comparing Methods of Tuning Adaptively Randomized Trials

The simplest Bayesian adaptive randomization scheme is to randomize patients to a treatment with probability equal to the probability p that the treatment is better. We examine three variations on adaptive randomization which are used to compromise between this scheme and equal randomization. The first variation is to apply a power transformation to p to obtain randomization probabilities. The second is to clip p to live within specified lower and upper bounds. The third is to begin the trial with a burn-in period of equal randomization. We illustrate how each approach effects statistical power and the number of patients assigned to each treatment. We conclude with recommendations for designing adaptively randomized clinical trials. Comparing methods of tuning adaptively randomized trials John D. Cook∗ January 9, 2007 Abstract The simplest Bayesian adaptive randomization scheme is to randomize patients to a treatment with probability equal to the probability p that the treatment is better. We examine three variations on adaptive randomization which are used to compromise between this scheme and equal randomization. The first variation is to apply a power transformation to p to obtain randomization probabilities. The second is to clip p to live within specified lower and upper bounds. The third is to begin the trial with a burn-in period of equal randomization. We illustrate how each approach effects statistical power and the number of patients assigned to each treatment. We conclude with recommendations for designing adaptively randomized clinical trials. ∗University of Texas M. D. Anderson Cancer Center, [email protected] simplest Bayesian adaptive randomization scheme is to randomize patients to a treatment with probability equal to the probability p that the treatment is better. We examine three variations on adaptive randomization which are used to compromise between this scheme and equal randomization. The first variation is to apply a power transformation to p to obtain randomization probabilities. The second is to clip p to live within specified lower and upper bounds. The third is to begin the trial with a burn-in period of equal randomization. We illustrate how each approach effects statistical power and the number of patients assigned to each treatment. We conclude with recommendations for designing adaptively randomized clinical trials. ∗University of Texas M. D. Anderson Cancer Center, [email protected]