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In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm
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Nichols, Linda, Taverner, Tom, Crowe, Francesca, Richardson, Sylvia, Yau, Christopher, Kiddle, Steven, Kirk, Paul, Barrett, Jessica, Nirantharakumar, Krishnarajah, Griffin, Simon, Edwards, Duncan and Marshall, Tom (2022) In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm. Journal of Clinical Epidemiology, 152 . pp. 164-175. doi:10.1016/j.jclinepi.2022.10.011 ISSN 0895-4356.
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Official URL: https://doi.org/10.1016/j.jclinepi.2022.10.011
Abstract
To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering. We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated in the medical records of male patients, aged 65 to 84 from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability was in 400 bootstrap samples. In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54). LCA achieved higher aRI than other clustering algorithms. [Abstract copyright: Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.]
| Item Type: | Journal Article | |||||||||
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| Subjects: | R Medicine > R Medicine (General) R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||
| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Comorbidity, Chronic diseases, Cluster analysis -- Computer programs, Medical records -- Data processing, Latent structure analysis, Latent variables | |||||||||
| Journal or Publication Title: | Journal of Clinical Epidemiology | |||||||||
| Publisher: | Elsevier Inc. | |||||||||
| ISSN: | 0895-4356 | |||||||||
| Official Date: | December 2022 | |||||||||
| Dates: |
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| Volume: | 152 | |||||||||
| Page Range: | pp. 164-175 | |||||||||
| DOI: | 10.1016/j.jclinepi.2022.10.011 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 8 December 2022 | |||||||||
| Date of first compliant Open Access: | 8 December 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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