TITLE: Bayesian Adaptive Statistical Approaches for Accelerating Drug and Medical Device Approvals in Rare Disease
SPEAKERS: Bradley P. Carlin, PharmaLex – US
MODERATOR: Din Chen
Abstract:
It is estimated that more than 30 million people in the U.S. are impacted by acknowledged rare diseases, which now number over 7000. Sadly, the development of clinical trials to study such diseases has been hampered by the inherently small patient populations available for study, as well as poor understanding of the natural history of such diseases. Since August 2018, the use of Bayesian and other nontraditional approaches in this area has been fostered by the FDA’s Complex Innovative Trial Design (CID) Pilot Meeting Program. More recently, in May 2022, the FDA Center for Drug Evaluation and Research (CDER) launched its Accelerating Rare disease Cures (ARC) Program, which seeks to speed and increase the development of effective and safe treatment options addressing the unmet needs of patients with rare diseases.
Thanks to the emergence of Markov chain Monte Carlo (MCMC) computational methods in the 1990s, Bayesian methods now have a more than 25-year history of utility in statistical and biostatistical design and analysis. Such methods are especially useful in the area of rare disease, where the inherently small sample sizes mean that standard approaches are either unethical, hopelessly underpowered, or both. In this tutorial, after a brief review of some Bayesian basics, we consider recent developments in Bayesian adaptive methods for cautiously borrowing strength from historical datasets, an approach now commonly used to boost power in rare disease trials. Here, the notion of effective sample size is important to judge the relative importance and impacts of the various data sources. Techniques specific to rare and pediatric diseases will be discussed, as will an approach for optimally selecting the timing of an interim look at the data. On the drug side, the use of PK/PD data to expand the range of useful auxiliary information will be explored. We will offer an application of such borrowing to platform trials, and also describe methods for borrowing from a subject’s own disease natural history data. We also consider the problem of borrowing strength from observational and other real world data (RWD), where propensity score matching offers a way to correct for possible biases arising from the lack of randomization. Throughout, we illustrate with practical examples from the instructor’s own consulting practice, which has included both device and drug approvals. We also comment on relevant recent developments in Bayesian computing, especially the R-INLA package, an approximate Bayesian approach that offers a two-order of magnitude speed-up over traditional MCMC approaches, enormously helpful when simulating design operating characteristics for sponsors and regulators.
Keywords: Auxiliary data; Bayesian statistics; Commensurate prior; Complex Innovative Design (CID); Medical device trials; Natural history data; Pediatric disease; Power prior; Rare disease; Real world data (RWD); Robust mixture prior.
Instructors’ Biography:
Brad Carlin is a statistical researcher, methodologist, consultant, and instructor. In addition to serving as founder and president of Counterpoint, he is also Senior Advisor for Data Science and Statistics at PharmaLex, an international pharmaceutical consulting firm. Prior to this, he spent 27 years on the faculty of the Division of Biostatistics at the University of Minnesota School of Public Health, serving as division head for 7 of those years. He has also held visiting positions at Carnegie Mellon University, Medical Research Council Biostatistics Unit, Cambridge University (UK), Medtronic Corporation, HealthPartners Research Foundation, the M.D Anderson Cancer Center, and AbbVie Pharmaceuticals. He has published more than 185 papers in refereed books and journals, and has co-authored three popular textbooks: “Bayesian Methods for Data Analysis” with Tom Louis, “Hierarchical Modeling and Analysis for Spatial Data” with Sudipto Banerjee and Alan Gelfand, and “Bayesian Adaptive Methods for Clinical Trials” with Scott Berry, J. Jack Lee, and Peter Muller. From 2006-2009 he served as editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA). During his academic career, he served as primary dissertation adviser for 20 PhD students.
Dr. Carlin has extensive experience teaching short courses and tutorials, and won both teaching and mentoring awards from the University of Minnesota. During his spare time, Brad is a health musician and bandleader, providing keyboards and vocals in a variety of venues.