Challenges and Opportunities Posed by 'small Data': Bayesian Methods for Early-Phase Clinical Trials in Rare Diseases

• Activity Number: 633
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Qiuling Ally He*
• Companies: Novartis Institutes for BioMedical Research
• Keywords: Bayesian adaptive design ; rare diseases ; early phase clinical trials

Abstract

Developing treatments for rare diseases is one of the fastest growing and most challenging areas in clinical research. Drug development programs and clinical trials in rare diseases pose unique design challenges, for example, the limited pool of patients available for enrollment; the need to conduct first-in-man trials in a patient population due to a drug safety profile unacceptable for healthy volunteers; the desire that even participants in an early phase trial should have chance of receiving a therapeutic benefit, etc.. Bayesian inference with its emphasis on adaptive learning and incorporation of prior beliefs regarding the dose-efficacy tradeoff can be successfully used to address these challenges, especially in the early development setting where the emphasis is on exploration and learning rather than confirmation and labeling claims. In this presentation, opportunities and challenges of applying Bayesian adaptive designs to early phase studies in rare diseases are summarized. Case studies and simulation results will be presented to demonstrate advantages of the Bayesian methods.