Abstract
Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.
Original language | English (US) |
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Pages (from-to) | 1273-1287 |
Number of pages | 15 |
Journal | Statistical Methods in Medical Research |
Volume | 30 |
Issue number | 5 |
DOIs | |
State | Published - May 2021 |
Keywords
- Adaptive clip method
- adaptive randomization
- multi-arm trials
- patient horizon
- utility
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management
MD Anderson CCSG core facilities
- Biostatistics Resource Group