\nWho is this event intended fo r? \;Biostatisticians and drug developers in pharmaceutical i ndustry\, as well as students\, people working in academia and regulators who are involved in/interested in learning about the challenges and benefi ts of using RWD in clinical trials in small populations and rare diseases.
\nWhat is the benefit of attending? \;Attendees will gain insights into different uses of RWD in small populations and ra re diseases.
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\nRegistration
\nThis event is free to attend for both Members of PSI and Non-Members. To register your place\, please&n bsp\;\n \;
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Overview\n
In this webinar we will review the range of statistical methodolog
ies used to harness the potential of Real-World Data (RWD) in clinical dev
elopment\, particularly in the context of rare diseases and small populati
ons like paediatrics. The session will include theoretical understanding a
nd practical case studies\, with a special focus on Bayesian methods and c
ausal inference. \; \; \;
\n
\nTim Friede will pres
ent how randomized controlled trials can benefit from the inclusion of rea
l world data\, especially in rare diseases. There are various promising wa
ys of linking data from RCTs and RWD. \; Therefore\, a more routine jo
int consideration of RCT and RWD data appears desirable\, in particular in
rare diseases. \;
\n
\nBrad Carlin will provide a brief rev
iew of the Bayesian adaptive approach to clinical trial design and analysi
s\, and then will discuss a variety of areas in which Bayesian methods off
er a better (and perhaps the only) path to regulatory approval. \; Top
ics to be covered are expected to include:
\n- Leveraging historical
controls and other auxiliary data \;
\n- Methods for rare and ped
iatric disease \;
\n- Causal inference tools to incorporate RWD/R
WE
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\nFollowing the talks there will be discussion and Q&\;
A.
Speaker details
\n| \n Speaker \n | \n
\n Biography \n | \n \n Abstract \n | \n
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\n Since J anuary 2010 Tim Friede is Professor of Biostatistics at the University Med ical Center Gö\;ttingen where he heads up the Department of Medical St atistics. 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 Mathema tics and Statistics at Lancaster University as NHS Training Fellow in Medi cal Statistics and was later promoted to Lecturer in Biostatistics. From 2 004 on he worked for Novartis Pharma AG\, Basel before joining Warwick Med ical School as Associate Professor of Medical Statistics in October 2006. Tim Friede's methodological research interests are in clinical biostatisti cs 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 cardiovasc ular medicine. \n | \n \n Combining randomized controlled trials and real world data in rare diseases \nR andomized controlled trials (RCTs) are the gold standard for evaluating in terventions. However\, they are often considered to be difficult to conduct and may therefore suffer from small sample sizes. Here we demonstrate how R CTs can benefit from the inclusion of real world data (RWD). More specifica lly\, hierarchical models for evidence synthesis can be utilized to combin e 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 R CT and can also be very efficient when the RCT and registry are evaluated jo intly. In conclusion\, there are various promising ways of linking data fr om RCTs and RWD. Therefore\, a more routine joint consideration of RCT and RWD data appears desirable\, in particular in rare diseases. This is join t work with Christian Rö\;ver and Tim Mathes. \n&nbs p\; \n\; \n\; \n | \n
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\n Brad Carli n is a statistical researcher\, methodologist\, consultant\, and instructo r. He currently serves as Senior Advisor for Data Science and Statistics a t Cencora-PharmaLex\, an international pharmaceutical consulting firm. Pri or to this\, he spent 27 years on the faculty of the Division of Biostatis tics at the University of Minnesota School of Public Health\, serving as d ivision 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 Resear ch Foundation\, the M.D Anderson Cancer Center\, and AbbVie Pharmaceutical s. He has published more than 190 papers in refereed books and journals\, and has co-authored three popular textbooks: &ldquo\;Bayesian Methods for Data Analysis&rdquo\; with Tom Louis\, &ldquo\;Hierarchical Modeling and A nalysis for Spatial Data&rdquo\; 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 o f Bayesian Analysis\, the official journal of the International Society fo r Bayesian Analysis (ISBA). During his academic career\, he served as prim ary dissertation adviser for 20 PhD students. Dr. Carlin has extensive exp erience teaching short courses and tutorials\, and won both teaching and m entoring 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. \n | \n \n 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 upt ake in regulatory science has been much slower\, due to the high premium t his 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 w illingness to consider more innovative statistical methods\, especially in settings where traditional methods are ill-suited or demonstrably inadequ ate. \nIn this talk\, after a very brief review of the B ayesian adaptive approach to clinical trial design and analysis\, we will discuss a variety of areas in which Bayesian methods offer a better (and p erhaps the only) path to regulatory approval. Topics to be covered are exp ected to include: \n Leveraging historical controls and other auxiliary data (power/commensurate/robust mixture priors) \n Methods for rare and pediatric disease (including those utili zing patient natural history data) \n Causal inference tools to incorporate real world data (RWD)/real world evidence (RWE)\, inc luding synthetic controls \n | \n
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