Bayesian Methods in Clinical Design

Benefits of Bayesian Methods in Clinical Design

Due to the expensive nature of clinical trials, more and more pharmaceutical companies are becoming interested in Bayesian methods; and with on-going algorithmic development and improved computational speeds, these methods are becoming increasingly accessible and accepted.

HERE'S WHY:

• Reduced costs - Making use of relevant prior information can reduce sample sizes (or shorten trial lengths) required to meet objectives, reducing overall development costs.  
• Improved trial design efficiency - Use of data as they are collected (either through interim analyses or continual reassessment methods) allows the trial design to be adapted to improve design efficiency. For example, ineffective treatment arms could be dropped; further treatment arms could be introduced; the trial could be stopped early (due to established futility /efficacy); or randomization to treatment could be altered to favor the more effective treatment. Such adaptations are attractive to both researchers and patients, by making more efficient use of patient resource and potentially treating patients more effectively.  In general adaptive clinical trial designs are easier to implement within the Bayesian framework.  

• Frequentist designs may not always work - Bayesian methods have particular advantages in rare disease scenarios where traditional sample sizes can be difficult – if not impossible - to achieve. The standard frequentist methodology of hypothesis testing in a clinical trial may not always be the best approach and Bayesian methods allow alternative approaches to be considered.