As clinical trial costs continue to rise, industry statisticians have faced increasing pressure to develop and utilize more efficient statistical techniques. Fortunately, regulators at FDA, EMA and elsewhere are increasingly comfortable with Bayesian statistical techniques, which permit formal borrowing of strength from expert opinion and auxiliary data, and yield full probabilistic inference regarding model quantities of interest. These approaches are increasingly being encouraged by FDA though its Complex Innovative Trial Design (CID) initiative, consistent with the 21st Century Cures Act, passed by the US Congress in late 2016, and PDUFA VI. Bayesian statistical methods facilitate adaptive dose-finding and randomization, and have a long history of success in early phase clinical trial settings where patients and other resources are scarce and/or where reliable external information is available. However, it's often unclear when and how much strength to borrow from external data sources, especially if they are historical, observational, or both.
In this webinar, after a very brief review of the Bayesian approach, we illustrate its use in simple data combination methods, including traditional two-step approaches, as well as ones using power priors, commensurate priors, and robust mixture priors for incorporating sensibly downweighted versions of the auxiliary information. 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, we explore the use of PK/PD data as well as clinical data on the natural history of the disease as examples of useful auxiliary information. We also mention the problem of borrowing strength from observational data, where propensity score matching offers a way to correct for possible biases arising from the lack of randomization.
Senior Advisor Data Science and Statistics, PharmaLex
Brad Carlin is a statistical researcher, methodologist, consultant, and instructor. Prior to joining PharmaLex, 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 published more than 185 papers in refereed books and journals, and has co-authored three popular textbooks on Bayesian statistical methods and their applications in spatial statistics and adaptive clinical trials. During his spare time, Brad is a health musician and bandleader, providing keyboards and vocals in a variety of venues. He is extremely excited to have joined the PharmaLex data science and statistics team.
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