Presented at the 17th Annual NEALS Meeting in Clearwater, Florida, October 3, 2018.

Objective: We demonstrate several applications to improve the efficiency of ALS drug development clinical trials, including a novel method of subgroup analysis, “detectable effect cluster analysis” (DEC) to identify subgroups with significant effect sizes.

Results: ALS models were developed using the PRO-ACT database. The models were utilized to create drug development applications, including a novel method of subgroup analysis, “detectable effect cluster analysis” (DEC) to identify subgroups with significant effect sizes. DEC analysis can reveal therapeutic effects hidden in a larger population, the enrichment, randomization and covariate adjustment tools can reduce sample sizes and/or increase the power of a study, and  the virtual control provides an objective measure of efficacy in early trials that can inform subsequent trial design. The tools have been discussed with the FDA.

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, Andrew Conklin, Jonavelle Cuerdo, Mike Keymer, David L. Ennist

2018-09-24-Ennist-2018-NEALS

 

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