Speaker

Biography

Abstract

Tim Friede

Since January 2010 Tim Friede is Professor of Biostatistics at the University Medical Center Göttingen where he heads up the Department of Medical Statistics. He graduated in Mathematics (Dipl.-Math.) from the University of Karlsruhe and obtained a PhD (Dr.sc.hum.) from the Faculty of Medicine at the University of Heidelberg. In 2001 he joined the Department of Mathematics and Statistics at Lancaster University as NHS Training Fellow in Medical Statistics and was later promoted to Lecturer in Biostatistics. From 2004 on he worked for Novartis Pharma AG, Basel before joining Warwick Medical School as Associate Professor of Medical Statistics in October 2006. Tim Friede's methodological research interests are in clinical biostatistics including designs for clinical trials (in particular flexible adaptive designs) and generalized evidence synthesis (including systematic reviews and meta-analyses) as well as applications in rare diseases and cardiovascular medicine.

Combining randomized controlled trials and real world data in rare diseases

Randomized controlled trials (RCTs) are the gold standard for evaluating interventions. However, they are often considered to be difficult to conduct and may therefore suffer from small sample sizes. Here we demonstrate how RCTs can benefit from the inclusion of real world data (RWD). More specifically, hierarchical models for evidence synthesis can be utilized to combine RWD and RCT data to increase the precision of the RCT effect estimate. In the comprehensive cohort study design, the RCT and the cohort study are carried out in parallel. It allows to assess the external avlidity of an RCT and can also be very efficient when the RCT and registry are evaluated jointly. In conclusion, there are various promising ways of linking data from RCTs and RWD. Therefore, a more routine joint consideration of RCT and RWD data appears desirable, in particular in rare diseases. This is joint work with Christian Röver and Tim Mathes.

Brad Carlin

Brad Carlin is a statistical researcher, methodologist, consultant, and instructor. He currently serves as Senior Advisor for Data Science and Statistics at Cencora-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 190 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, guitar, and vocals in a variety of venues.

Thanks to the sudden 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. However, their uptake in regulatory science has been much slower, due to the high premium this field places on Type I error control, and its historical reliance on p-values and other traditional frequentist statistical tools. Fortunately, recent actions by regulators at FDA and elsewhere have indicated a new willingness to consider more innovative statistical methods, especially in settings where traditional methods are ill-suited or demonstrably inadequate.

In this talk, after a very brief review of the Bayesian adaptive approach to clinical trial design and analysis, we will discuss a variety of areas in which Bayesian methods offer a better (and perhaps the only) path to regulatory approval. Topics to be covered are expected to include:

 Leveraging historical controls and other auxiliary data (power/commensurate/robust mixture priors)

 Methods for rare and pediatric disease (including those utilizing patient natural history data)

 Causal inference tools to incorporate real world data (RWD)/real world evidence (RWE), including synthetic controls