Presented at the 2019 MDA Clinical and Scientific Conference in Orlando, Florida on April 15, 2019.  Re-presented at the 71st Annual Meeting of the American Academy of Neurology in Philadelphia, Pennsylvania on May 7, 2019, and at the ENCALS Meeting 2019 in Tours, France on May 16, 2019.

Background: We previously developed regression models for total ALSFRS-R score, ALSFRS-R subscores, vital capacity (VC), and percent expected VC, and time-to-event models for loss of speech, use of wheelchair, gastrostomy, use of NIV (using time to 50% expected VC as a surrogate) and survival.

Objectives: To improve the efficiency of ALS drug development clinical trials.

Conclusions: The detectable effect cluster (DEC) analysis, enrichment, randomization/covariate and virtual control tools find “hot spots” of patients with demonstrable benefit, decrease trial heterogeneity, lower sample size/increase power, and provide an objective measure of efficacy for drug development trials in ALS. DEC analysis shows great promise in identifying subgroups within a failed trial that could have formed a successful trial. These applications represent a significant paradigm shift with broad implications for the conduct of trials in ALS in particular and can be extended to a range of neurodegenerative diseases.

Authors: Danielle Beaulieu, Albert A. Taylor, Jonavelle Cuerdo, Andrew Conklin, Mike Keymer, David L. Ennist

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