Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups

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

PLOS ONE, 2009, vol. 4, issue 9, 1-6

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

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0007107 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 07107&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0007107

DOI: 10.1371/journal.pone.0007107

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().