In clinical trials, we randomize to ensure the blind. But even the most secure randomization algorithms, which provide the least predictability, have a real risk of unblinding when implemented.
In a recent webinar, Calyx’s Head of Statistics and Product Support Services, Malcolm Morrissey shared his experience on how to identify the randomization implementation options that could lead to unblinding of the block size or partial unblinding of subject treatment.
Here he addresses some of the questions that arose during the webinar related to how Calyx mitigates this risk of selection bias at the site, as well as other practical issues associated with randomization, such as capping and trends in methodology.