The importance of cluster analysis for enhancing clinical practice: an example from irritable bowel syndrome

References

• Assmann SF, Pocock SJ, Enos LE, Kasten LE. (2000). Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet, 355, 1064–9
   PubMed Web of Science ®Google Scholar
• Bentall D, Pilgrim R. (1999). The medicalisation of misery: A critical realist analysis of the concept of depression. J Mental Health, 8, 261–74
   Google Scholar
• Breckenridge JN. (1989). Replicating cluster analysis: Method, consistency, and validity. Multivariate Behav Res, 24, 147–61
   PubMed Web of Science ®Google Scholar
• Drossman DA. (2016). Functional gastrointestinal disorders: History, pathophysiology, clinical features, and Rome IV. Gastroenterology, 150, 1262–79. e1262
   Web of Science ®Google Scholar
• Dziak JJ, Lanza ST, Tan X. (2014). Effect size, statistical power, and sample size requirements for the bootstrap likelihood ratio test in latent class analysis. Struct Equ Modeling, 21, 534–52
   PubMed Web of Science ®Google Scholar
• Everitt B, Landau S, Leese M, Stahl D. (2001). Cluster analysis. 2001. London: Arnold
   Google Scholar
• Finch WH, Bronk KC. (2011). Conducting confirmatory latent class analysis using M plus. Struct Equ Modeling, 18, 132–51
   Web of Science ®Google Scholar
• Fraley C, Raftery AE. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J, 41, 578–88
   Web of Science ®Google Scholar
• Hagenaars JA, McCutcheon AL. (2002). Applied latent class analysis. Edinburgh: Cambridge University Press
   Google Scholar
• Lanza ST, Rhoades BL. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prev Sci, 14, 157–68
   PubMed Web of Science ®Google Scholar
• Merikangas KR, Wicki W, Angst J. (1994). Heterogeneity of depression. Classification of depressive subtypes by longitudinal course. Br J Psychiatry, 164, 342–8
   PubMed Web of Science ®Google Scholar
• Milligan GW. (1996). Clustering validation: Results and implications for applied analyses. In: De Soete G, Arabie P, Hubert LJ, eds. Clustering and classification. River Edge, NJ: World Scientific Publ., 341–75
   Google Scholar
• Nathan PE, Langenbucher J. (2003). Diagnosis and classification. In: Stricker Bl G, Widiger TA, eds. Handbook of psychology: Clinical psychology (Vol. 8). New York: Wiley, 3426
   Google Scholar
• Nylund KL, Asparouhov T, Muthén BO. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct Equ Modeling, 14, 535–69
   Web of Science ®Google Scholar
• Polster A, Van Oudenhove L, Jones M, et al. (2017). Mixture model analysis identifies irritable bowel syndrome subgroups characterised by specific profiles of gastrointestinal, extraintestinal somatic and psychological symptoms. Aliment Pharmacol Ther, 46, 529–39
   PubMed Web of Science ®Google Scholar
• Song S, Jason LA. (2005). A population-based study of chronic fatigue syndrome (CFS) experienced in differing patient groups: An effort to replicate Vercoulen et al.'s model of CFS. J Mental Health, 14, 277–89
   Google Scholar
• Spence MJ, Moss-Morris R. (2007). The cognitive behavioural model of irritable bowel syndrome: A prospective investigation of patients with gastroenteritis. Gut, 56, 1066–71
   PubMed Web of Science ®Google Scholar
• Spiegel BM, Farid M, Esrailian E, et al. (2010). Is irritable bowel syndrome a diagnosis of exclusion?: A survey of primary care providers, gastroenterologists, and IBS experts. Am J Gastroenterol, 105, 848–58
   PubMed Web of Science ®Google Scholar
• Stahl D, Sallis H. (2012). Model‐based cluster analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 4, 341–58
   Google Scholar
• Taylor RR, Leonard JA, Schoeny ME. (2001). Evaluating latent variable models of functional somatic distress in a community-based sample. J Mental Health, 10, 335–49
   Google Scholar
• Tekle FB, Gudicha DW, Vermunt JK. (2016). Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models. Adv Data Anal Classif, 10, 209–24
   Web of Science ®Google Scholar
• Van den Berg MD, Oldehinkel AJ, Bouhuys AL, et al. (2001). Depression in later life: Three etiologically different subgroups. J Affect Disord, 65, 19–26
   PubMed Web of Science ®Google Scholar
• Wakefield JC. (2010). Misdiagnosing normality: Psychiatry's failure to address the problem of false positive diagnoses of mental disorder in a changing professional environment. J Mental Health, 19, 337–51
   PubMed Web of Science ®Google Scholar