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A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging.
- Source :
-
Journal of clinical epidemiology [J Clin Epidemiol] 2024 Aug; Vol. 172, pp. 111435. Date of Electronic Publication: 2024 Jun 18. - Publication Year :
- 2024
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Abstract
- Objectives: To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters.<br />Study Design and Setting: Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians.<br />Results: Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity.<br />Conclusion: These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.<br />Competing Interests: Declaration of competing interest The authors declare no financial conflicts of interest.<br /> (Copyright © 2024 Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1878-5921
- Volume :
- 172
- Database :
- MEDLINE
- Journal :
- Journal of clinical epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 38901709
- Full Text :
- https://doi.org/10.1016/j.jclinepi.2024.111435