<p>Detectable Effect Cluster Analysis: A Novel Machine-Learning Based Clinical Trial Subgroup Analysis Tool</p>

Danielle Beaulieu; Albert A. Taylor; Andrew  Conklin; Jonavelle Cuerdo; Dustin Pierce; Mike Keymer; David L. Ennist

doi:10.7490/f1000research.1117619.1

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Beaulieu D, Taylor AA, Conklin A et al. Detectable Effect Cluster Analysis: A Novel Machine-Learning Based Clinical Trial Subgroup Analysis Tool [version 1; not peer reviewed]. F1000Research 2019, 8:1883 (poster) (https://doi.org/10.7490/f1000research.1117619.1)

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Detectable Effect Cluster Analysis: A Novel Machine-Learning Based Clinical Trial Subgroup Analysis Tool

Danielle Beaulieu, Albert A. Taylor, Andrew Conklin, Jonavelle Cuerdo, Dustin Pierce, Mike Keymer, David L. Ennist¹

Published 08 Nov 2019 (https://doi.org/10.7490/f1000research.1117619.1)

Author Affiliations

¹ Origent Data Sciences, Inc., USA

Copyright © 2019 Beaulieu D et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 08 Nov 2019

Detectable Effect Cluster Analysis: A Novel Machine-Learning Based Clinical Trial Subgroup Analysis Tool
[version 1; not peer reviewed]

Danielle Beaulieu, Albert A. Taylor, Andrew Conklin, Jonavelle Cuerdo, Dustin Pierce, Mike Keymer, David L. Ennist¹

Author Affiliations

¹ Origent Data Sciences, Inc., USA

Presented at

Northeastern Amyotrophic Lateral Sclerosis Consortium (NEALS) Meeting 2019

Abstract

Competing Interests

Salary, stock and patent application.

Keywords

machine learning, ALS, subgroup analysis, clinical trial design

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