P Thall1, P Fox2, J Wathen3. 1. Department of Biostatistics, U.T. M.D. Anderson Cancer Center, Houston rex@mdanderson.org. 2. Department of Biostatistics, U.T. M.D. Anderson Cancer Center, Houston. 3. Model Based Drug Development, Janssen Research & Development, Titusville, USA.
Abstract
BACKGROUND: In recent years, various outcome adaptive randomization (AR) methods have been used to conduct comparative clinical trials. Rather than randomizing patients equally between treatments, outcome AR uses the accumulating data to unbalance the randomization probabilities in favor of the treatment arm that currently is superior empirically. This is motivated by the idea that, on average, more patients in the trial will be given the treatment that is truly superior, so AR is ethically more desirable than equal randomization. AR remains controversial, however, and some of its properties are not well understood by the clinical trials community. MATERIALS AND METHODS: Computer simulation was used to evaluate properties of a 200-patient clinical trial conducted using one of four Bayesian AR methods and compare them to an equally randomized group sequential design. RESULTS: Outcome AR has several undesirable properties. These include a high probability of a sample size imbalance in the wrong direction, which might be surprising to nonstatisticians, wherein many more patients are assigned to the inferior treatment arm, the opposite of the intended effect. Compared with an equally randomized design, outcome AR produces less reliable final inferences, including a greatly overestimated actual treatment effect difference and smaller power to detect a treatment difference. This estimation bias becomes much larger if the prognosis of the accrued patients either improves or worsens systematically during the trial. CONCLUSIONS: AR produces inferential problems that decrease potential benefit to future patients, and may decrease benefit to patients enrolled in the trial. These problems should be weighed against its putative ethical benefit. For randomized comparative trials to obtain confirmatory comparisons, designs with fixed randomization probabilities and group sequential decision rules appear to be preferable to AR, scientifically, and ethically.
RCT Entities:
BACKGROUND: In recent years, various outcome adaptive randomization (AR) methods have been used to conduct comparative clinical trials. Rather than randomizing patients equally between treatments, outcome AR uses the accumulating data to unbalance the randomization probabilities in favor of the treatment arm that currently is superior empirically. This is motivated by the idea that, on average, more patients in the trial will be given the treatment that is truly superior, so AR is ethically more desirable than equal randomization. AR remains controversial, however, and some of its properties are not well understood by the clinical trials community. MATERIALS AND METHODS: Computer simulation was used to evaluate properties of a 200-patient clinical trial conducted using one of four Bayesian AR methods and compare them to an equally randomized group sequential design. RESULTS: Outcome AR has several undesirable properties. These include a high probability of a sample size imbalance in the wrong direction, which might be surprising to nonstatisticians, wherein many more patients are assigned to the inferior treatment arm, the opposite of the intended effect. Compared with an equally randomized design, outcome AR produces less reliable final inferences, including a greatly overestimated actual treatment effect difference and smaller power to detect a treatment difference. This estimation bias becomes much larger if the prognosis of the accrued patients either improves or worsens systematically during the trial. CONCLUSIONS: AR produces inferential problems that decrease potential benefit to future patients, and may decrease benefit to patients enrolled in the trial. These problems should be weighed against its putative ethical benefit. For randomized comparative trials to obtain confirmatory comparisons, designs with fixed randomization probabilities and group sequential decision rules appear to be preferable to AR, scientifically, and ethically.
Authors: Rebecca S Slack Tidwell; S Andrew Peng; Minxing Chen; Diane D Liu; Ying Yuan; J Jack Lee Journal: Clin Trials Date: 2019-08-26 Impact factor: 2.486
Authors: Lillian L Siu; S Percy Ivy; Erica L Dixon; Amy E Gravell; Steven A Reeves; Gary L Rosner Journal: Clin Cancer Res Date: 2017-09-01 Impact factor: 12.531