title: Identifying subgroups of patients using latent class analysis: should we use a single-stage or a two-stage approach? A methodological study using a cohort of patients with low back pain.
abstract: Heterogeneity in patients with low back pain (LBP) is well recognised and different approaches to subgrouping have been proposed. Latent Class Analysis (LCA) is a statistical technique that is increasingly being used to identify subgroups based on patient characteristics. However, as LBP is a complex multi-domain condition, the optimal approach when using LCA is unknown. Therefore, this paper describes the exploration of two approaches to LCA that may help improve the identification of clinically relevant and interpretable LBP subgroups.
  
  
  
  From 928 LBP patients consulting a chiropractor, baseline data were used as input to the statistical subgrouping. In a single-stage LCA, all variables were modelled simultaneously to identify patient subgroups. In a two-stage LCA, we used the latent class membership from our previously published LCA within each of six domains of health (activity, contextual factors, pain, participation, physical impairment and psychology) (first stage) as the variables entered into the second stage of the two-stage LCA to identify patient subgroups. The description of the results of the single-stage and two-stage LCA was based on a combination of statistical performance measures, qualitative evaluation of clinical interpretability (face validity) and a subgroup membership comparison.
  
  
  
  For the single-stage LCA, a model solution with seven patient subgroups was preferred, and for the two-stage LCA, a nine patient subgroup model. Both approaches identified similar, but not identical, patient subgroups characterised by (i) mild intermittent LBP, (ii) recent severe LBP and activity limitations, (iii) very recent severe LBP with both activity and participation limitations, (iv) work-related LBP, (v) LBP and several negative consequences and (vi) LBP with nerve root involvement.
  
  
  
  Both approaches identified clinically interpretable patient subgroups. The potential importance of these subgroups needs to be investigated by exploring whether they can be identified in other cohorts and by examining their possible association with patient outcomes. This may inform the selection of a preferred LCA approach.
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, 5230, Odense M, Denmark. amnielsen@health.sdu.dk.
authors.forename: Anne Molgaard
authors.initials: AM
authors.lastname: Nielsen
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, 5230, Odense M, 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, 5230, Odense M, Denmark.|Nordic Institute of Chiropractic and Clinical Biomechanics, University of Southern Denmark, 5230, Odense M, Denmark.
authors.forename: Lise
authors.initials: L
authors.lastname: Hestbaek
authors.affiliation: Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, 79104, Freiburg, Germany.
authors.forename: Werner
authors.initials: W
authors.lastname: Vach
authors.affiliation: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, 5230, Odense M, Denmark.|Nordic Institute of Chiropractic and Clinical Biomechanics, University of Southern Denmark, 5230, Odense M, Denmark.
authors.forename: Alice
authors.initials: A
authors.lastname: Kongsted
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D017116:Low Back Pain / Q000145:classification*; D008297:Male; D008875:Middle Aged; D013223:Statistics as Topic
keywords: Classification; Data mining; Latent class analysis; Low back pain; Subgrouping