Using an Onset-Anchored Bayesian Hierarchical Model to Improve Predictions for Amyotrophic Lateral Sclerosis Disease Progression

• Activity Number: 243 - Contributed Poster Presentations: Biopharmaceutical Section
• Type: Contributed
• Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Alex Karanevich*
• Keywords: ALS ; hierarchical modelling ; onset-anchor ; Bayes modelling ; longitudinal data

Abstract

Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, is a rare disease with extreme between-subject variability, especially with respect to rate of disease progression. This combined with a typically small number of longitudinal measurements per subject makes modelling a subject's disease progression, which is measured by the ALS Functional Rating Scale (ALSFRS), very difficult. Consider the problem of predicting a subject's ALSFRS score at 9 or 12 months after a given time-point. Here we investigate how leveraging an additional artificial data-point using time of disease onset can improve this prediction. The predictive mean-square-error using this onset-anchor is drastically reduced when compared to the MSE provided by either a Bayesian hierarchical linear model (similar to a mixed-effects model) or a Bayesian mixture model.