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Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups

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  • Jeban Ganesalingam
  • Daniel Stahl
  • Lokesh Wijesekera
  • Clare Galtrey
  • Christopher E Shaw
  • P Nigel Leigh
  • Ammar Al-Chalabi

Abstract

Background: Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. Methods: Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. Results: The best model generated five distinct phenotypic classes that strongly predicted survival (p

Suggested Citation

  • Jeban Ganesalingam & Daniel Stahl & Lokesh Wijesekera & Clare Galtrey & Christopher E Shaw & P Nigel Leigh & Ammar Al-Chalabi, 2009. "Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups," PLOS ONE, Public Library of Science, vol. 4(9), pages 1-6, September.
    • Handle: RePEc:plo:pone00:0007107
    DOI: 10.1371/journal.pone.0007107
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    1. Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
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