title: 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.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
authors.forename: Anne Molgaard
authors.initials: AM
authors.lastname: Nielsen
authors.affiliation: Center for Medical Biometry and Medical Informatics, Medical Center, University of Freiburg, Freiburg, Germany.
authors.forename: Werner
authors.initials: W
authors.lastname: Vach
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia.
authors.forename: Peter
authors.initials: P
authors.lastname: Kent
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; Nordic Institute of Chiropractic and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
authors.forename: Lise
authors.initials: L
authors.lastname: Hestbaek
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; Nordic Institute of Chiropractic and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
authors.forename: Alice
authors.initials: A
authors.lastname: Kongsted
keywords: classification; clinical interpretability; data mining; low back pain; questionnaire; subgrouping