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

BackgroundLatent 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 methodsBaseline 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.ResultsFor 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.ConclusionIn 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.

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

“…In the single-stage LCA , all variables were entered into the analysis simultaneously. In the two-stage LCA , identical baseline variables had been used in a previous first stage LCA [ 27 ] to identify domain-specific patient categorisations within six domains of health (activity, contextual factors, pain, participation, physical impairment and psychology), and these categorical variables comprised the input to the second stage LCA. The descriptive comparison of the resulting patient subgroups from 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.…”

“…LCA was performed within each of the health domains resulting in the six domain-specific patient categorisations. Based on the category for which their posterior probability was highest, patients were assigned to one of the categories within each health domain [ 27 ]. Subsequently, each category was given a descriptive label based on the main distribution of characteristics that distinguished each category from the others in the same health domain, using terms such as ‘more unemployed’, ‘higher BMI’ or by describing the span of the most frequently observed ages.…”

Exploring conceptual preprocessing for developing prognostic models: a case study in low back pain patients

“…knowledge into the development process in addition to selecting potential prognostic factors and selecting the type of rule. A recent project using a systematic approach to develop clinically relevant subgroupings of low back pain patients based on a large set of potential prognostic factors, as well as patients, provides a unique opportunity to study this question [6,7]. In the process of developing these subgroupings, the outcome was not taken into consideration, hence, allowing us to check the prognostic capacity of the obtained groupings in an unbiased manner.…”

Comorbidity Subgroups Among Medicare Beneficiaries Seeking Health Care for Musculoskeletal Pain

“…After selecting a best-fit latent class model, individuals were assigned group membership based on highest posterior probability estimates. We reported that median posterior probabilities used to classify each individual (32). These values provide an indication of ambiguity surrounding subgroup membership, where probabilities close to one are preferable.…”