On January 31, 2019 we released our third webinar, Using Disease Progression Modeling to Guide Decision Making in Rare Disease Clinical Trials presented by Dr. Melanie Quintana.
One common challenge in designing clinical trials in rare and progressive disease is understanding expected rates and variability of natural progression in the different patient populations that are likely to be enrolled in the trial. If the trial population is largely made up of an early staged population or a late stage population there may be little expected progression in terms of key clinical outcome measures over the course of the trial and little hope for demonstrating differences in progression between treatment and control patients. The development of clinical disease progression models within this context provides a better understanding of key clinical outcome measures that capture progression over the entire course of the disease and within different patient populations.
This webinar will focus on providing an understanding of the unique challenges that are encountered in designing clinical trials in rare progressive disease and how the development of clinical disease progression models can help to overcome some of those challenges. Examples will be given to demonstrate the usefulness of disease progression modeling in conjunction with natural history data to guide decision making for key trial parameters. We will also demonstrate how clinical disease progression models can be used as a powerful analysis tool for determining if a novel therapy is effective in altering disease progression.
Melanie Quintana, PhD is a statistical scientist at Berry Consultants where she designs Bayesian adaptive clinical trials across a wide range of therapeutic areas including numerous examples in rare and progressive disease. Her work in rare and progressive disease focuses on the development of disease progression models to better understand natural disease progression and to quantify evidence of treatment-related disease modification within clinical trials.