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

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• 作者：Anne Molgaard Nielsen ; Peter Kent ; Lise Hestbaek…
• 关键词：Low back pain ; Classification ; Data mining ; Latent class analysis ; Subgrouping
• 刊名：BMC Musculoskeletal Disorders
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：18
• 期：1
• 全文大小：2160KB
• 刊物主题：Orthopedics; Rehabilitation; Rheumatology; Sports Medicine; Internal Medicine; Epidemiology;
• 出版者：BioMed Central
• ISSN：1471-2474
• 卷排序：18

文摘

BackgroundHeterogeneity 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.