Selecting the Parameters of a Dynamic Randomization

By Scott Hamilton, PhD

Most statisticians would agree that dynamic randomization results in superior treatment group balancing over list-based stratified permuted blocked randomization, thereby increasing the precision in a randomized clinical trial. There have been several studies and review articles that provide ample evidence in favor of dynamic randomization.

The motivation for this article was to dissipate the perceptions of complexity by elucidating the details of our process for choosing the randomization parameters. We also provide guidance for how we implement dynamic randomization for clinical trials in randomization and trial supply management (RTSM) systems.

At Bracket, our mission is to bring quality, resourcefulness, and dependability to the medicine development process. Improvements in the ability to implement sound and robust dynamic randomization systems provide us an easy landscape to utilize modern simulation techniques and graphical displays of quantitative data for making decisions about the biased‐coin probabilities and weights. Future discussions will explore more subtle and flexible methods to biased‐coin and weighting in dynamic randomization.

Read the full article from Applied Clinical Trials here