Ruben van Eijk is an MD and biostatistician appointed as Assistant Professor at the University Medical Center Utrecht (UMCU), The Netherlands. He obtained his Ph.D. in neurology/biostatistics, entitled: “Optimizing the design and conduct of clinical trials for ALS”. He was a Visiting Scholar at the Center for Innovative Study Design and Department of Biomedical Data Sciences, Stanford University (Jan. 2021 to Jan. 2022). His current research focuses on new statistical models to combine survival and longitudinal data, integration of real-world evidence into drug development, as well as developing new endpoints that addresses the multidimensional nature of ALS and differences in patient preference.
This talk will illustrate the use of a predictive algorithm in clinical trials for Amyotrophic Lateral Sclerosis (ALS). A key challenge in ALS is the variability between patients in clinical symptoms, and the reliance on clinical outcomes to determine drug efficacy. I will illustrate how a prediction model can be used to select patients more efficiently for clinical trials, thereby reducing heterogeneity in the trial population and improving the precision of the study. This method is based on the ENCALS survival prediction model, a model that has been developed in over 11k patients living with ALS originating from 14 independent cohorts across Europe. The presentation will conclude with implementation considerations to use the proposed methodology in practice and actual clinical trials.
