Peter Kent|Rikke Krüger Jensen|Alice Kongsted
A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB
University of Southern Denmark|Lillebaelt Hospital|University of Southern Denmark|University of Southern Denmark
various methodological approaches identifying clinically important subgroups one method identify clusters characteristics differentiate people crosssectional andor longitudinal data using cluster analysis ca latent class analysis lca scarcity headtohead comparisons inform choice clustering method might suitable particular clinical datasets research questions therefore aim study perform headtohead comparison three commonly available methods spss twostep ca latent gold lca snob lcathe performance three methods compared quantitatively using number subgroups detected classification probability individuals subgroups reproducibility results ii qualitatively using subjective judgments programs ease use interpretability presentation resultswe analysed five real datasets varying complexity secondary analysis data research projects three datasets contained mri findings n 2060 20810 vertebral disc levels one dataset contained pain intensity data collected 52 weeks text sms messaging n 1121 people last dataset contained range clinical variables measured low back pain patients n 543 people four artificial datasets n 1000 containing subgroups varying complexity also analysed testing ability clustering methods detect subgroups correctly classify individuals subgroup membership knownthe results real clinical datasets indicated number subgroups detected varied certainty classifying individuals subgroups varied findings perfect reproducibility programs easier use interpretability presentation findings also varied results artificial datasets indicated three clustering methods showed nearperfect ability detect known subgroups correctly classify individuals subgroupsour subjective judgement latent gold offered best balance sensitivity subgroups ease use presentation results datasets recognise different clustering methods may suit types data clinical research questions