Delivered by: Melanie Quintana
Description: We describe a Bayesian repeated measures model based on quantitative muscle strength data from a prospective Natural History Study at the NIH that was developed to determine disease progression and design clinical trials for GNE myopathy, a rare and slowly progressive muscle disease. The GNE disease progression model can be used to better understand natural disease progression, to guide decision making for key clinical trial design parameters, and to provide an analysis method for determining if a novel therapy is effective in altering disease progression.
Bio: Melanie Quintana is a Senior Statistical Scientist at Berry Consultants, where she specializes in designing Bayesian adaptive clinical trials across a wide range of therapeutic areas. Her work includes numerous examples in designing platform trials and clinical trials in rare and progressive disease with a focus on developing models of disease progression to design better and more powerful clinical trials. Before joining Berry Consultants, she earned her Ph.D. in Statistics from Duke University and went on to pursue a Postdoc in Biostatistics at The University of Southern California.
The webinar is moderated by: Michela Onali, a patient representative from “Gli Equilibristi HIBM”, also member of ERN Euro-NMD’s ePAG, Education Board, Muscle Working Group and Guidelines Core Group.