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Using existing questionnaires in latent class analysis: should we use summary scores or single items as input? A methodological study using a cohort of patients with low back pain

Authors Nielsen A, Vach W, Kent P, Hestbaek L, Kongsted A

Received 29 December 2015

Accepted for publication 17 February 2016

Published 26 April 2016 Volume 2016:8 Pages 73—89


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Professor Henrik Toft Sørensen

Video abstract presented by Anne Molgaard Nielsen

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Anne Molgaard Nielsen,1 Werner Vach,2 Peter Kent,1,3 Lise Hestbaek,1,4 Alice Kongsted1,4

1Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; 2Center for Medical Biometry and Medical Informatics, Medical Center, University of Freiburg, Freiburg, Germany; 3School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia; 4Nordic Institute of Chiropractic and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

Background: Latent class analysis (LCA) is increasingly being used in health research, but optimal approaches to handling complex clinical data are unclear. One issue is that commonly used questionnaires are multidimensional, but expressed as summary scores. Using the example of low back pain (LBP), the aim of this study was to explore and descriptively compare the application of LCA when using questionnaire summary scores and when using single items to subgrouping of patients based on multidimensional data.
Materials and methods: Baseline data from 928 LBP patients in an observational study were classified into four health domains (psychology, pain, activity, and participation) using the World Health Organization’s International Classification of Functioning, Disability, and Health framework. LCA was performed within each health domain using the strategies of summary-score and single-item analyses. The resulting subgroups were descriptively compared using statistical measures and clinical interpretability.
Results: For each health domain, the preferred model solution ranged from five to seven subgroups for the summary-score strategy and seven to eight subgroups for the single-item strategy. There was considerable overlap between the results of the two strategies, indicating that they were reflecting the same underlying data structure. However, in three of the four health domains, the single-item strategy resulted in a more nuanced description, in terms of more subgroups and more distinct clinical characteristics.
Conclusion: In these data, application of both the summary-score strategy and the single-item strategy in the LCA subgrouping resulted in clinically interpretable subgroups, but the single-item strategy generally revealed more distinguishing characteristics. These results 1) warrant further analyses in other data sets to determine the consistency of this finding, and 2) warrant investigation in longitudinal data to test whether the finer detail provided by the single-item strategy results in improved prediction of outcomes and treatment response.

Keywords: classification, data mining, subgrouping, clinical interpretability, questionnaire, low back pain

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