Presented at the 30th International Symposium on ALS/MND in Perth, Australia on December 4, 2019.
Background: We previously developed machine-learning based ALS predictive models<sub>1-4</sub>, including a time to 50% expected vital capacity (VC50) model. Here we report on the use of the VC50 model to develop a novel subgroup analysis tool that we call “Detectable Effect Cluster” (DEC) analysis
Conclusion: Detectable Effect Cluster (DEC) analysis shows great promise in identifying subgroups within failed trials that could have formed the basis for successful trials. Importantly this approach allows investigators to explore the possibility of detecting subgroups in which a significant effect size is detectable both via reduced RMSE or increased Treatment Effect. One can also envision an adaptive trial with broad inclusion criteria, the purpose of which is to apply DEC analysis to identify a subgroup with a demonstrable treatment effect.
Authors: Danielle Beaulieu, Albert A. Taylor, Andrew Conklin, Jonavelle Cuerdo, Dustin Pierce, Mike Keymer, David L. Ennist2019-10-02-MNDA-OrigentPoster