American Statistical Association
Dr. Satrajit Roychoudhury is a Senior Director and a member of Statistical Research and Innovation group in Pfizer Inc. Prior to joining Pfizer Inc, he was a member of Statistical Methodology and consulting group in Novartis. He started his career as a research statistician in Schering Plough Research Institute (now Merck Co.). He has 12+ years of extensive experience in working with different phases of clinical trials. His primary expertise includes implementation of innovative statistical methodology in clinical trials. He has co-authored several publications/book chapters in this area and provided statistical training in major conferences. His area of research includes survival analysis, use of model-based approaches and Bayesian methods in clinical trials. Satrajit was a recipient of a Young Statistical Scientist Award from the International Indian Statistical Association in 2019.
Traditional confirmatory trials to demonstrate efficacy may not be ethical or feasible in small populations such as pediatric populations or orphan indications. Pediatric drug development often faces substantial challenges, including economic, logistical, and ethical barriers. Extrapolation of efficacy from the similar disease population in adults to pediatric populations is a possible solution for better and informed decision making in this situation. Extrapolating from existing data, also commonly referred to as bridging or borrowing strength. Leveraging data from adults to draw conclusion for the pediatric population provides additional precision to the estimand of interest. A Bayesian paradigm is a natural choice here as it provides a formal framework to synthesize what is known about a question of interest from adult data and the new information from the pediatric study using on the laws of probability. A common way of extrapolation is deriving informative prior based on adult data while analyzing the pediatric population. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. The European Medicines Agency (EMA) has proposed a general framework for extrapolation allowing for the incorporation of uncertainty about assumptions. In this talk we will explore a specific Bayesian approach that incorporates data from adult data in the analysis of pediatric clinical trials for robust decision making. The proposed methodology is complemented by simulation and real-life examples.