Bayesian Approaches for Incorporating Natural History Data in Studies of Rare and Pediatric Disease

Duration:  1 hour 


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Why watch

Summary

In this webinar, after a very brief review of Bayesian adaptive clinical trial methods, we introduce our Bayesian responder approach to one-arm clinical trials in rare disease modeling, investigating the impact of both static and transient placebo effects.  We then go on to describe two-arm versions that incorporate a small concurrent placebo group, but still borrow strength from the natural history data.  We also propose more traditional Bayesian changepoint models that specify a parametric functional form for the patient’s post-intervention trajectory, which in turn allow quantification of the treatment benefit in terms of the model parameters, rather than semiparametrically in terms of a response relative to some “null” model.  Our results indicate that our two-arm responder and changepoint methods can offer protection against placebo effects, improving power while controlling the trial’s Type I error rate.  We offer illustrations in the context of a clinical trial in a particular rare disease, where large patient-to-patient and visit-to-visit heterogeneity can be observed.  In such settings, our innovative Bayesian techniques facilitate increased power to detect an effect with respect to more classical methods.  We also offer advice regarding computational approaches in these settings, as well as our experience with key regulatory authorities, dialog with whom of course remains crucial in rare disease research.

Key Learnings

  •  Appreciation of the ethical, statistical, and regulatory issues involved in rare and pediatric disease clinical trials
  •  Guidance on how Bayesian approaches can use natural history data to supplement results of a one-arm study of a rare disease intervention 
  •  Comparison of one- and two-arm approaches, again highlighting tradeoffs between the need for unbiased estimation and concomitant ethical and practical constraints
speakers

A full hour of inspirational speaker(s)

Brad Carlin

Senior Advisor, Data Science, PharmaLex

Brad 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 excited and proud to be a part of the PharmaLex data science and statistics team.  

 

Arnaud Monseur

Senior Manager Statistics, PharmaLex

Arnaud holds a bachelor’s degree in Mathematics and a master degree in statistics and econometrics (Université Catholique de Louvain). He is a Senior Manager Statistics and Data Science at PharmaLex and has been at PharmaLex for the past 5 years. He has been primarily involved in various projects covering non-clinical, pre-clinical as well as clinical projects.