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Problems with latent class analysis to detect data-driven subtypes of depression

Molecular Psychiatry volume 23, pages 495–496 (2018)Cite this article

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Depressed patients differ considerably with respect to symptom profiles, course of illness and treatment response. These differences likely contribute to the on average low efficacy of treatment, and drive the search for more homogenous subtypes of depression in order to facilitate treatment decisions in clinical practice.1 Latent class analysis (LCA) presents a common statistical method in current depression research that aims to identify depressed patients with similar symptom profiles.2, 3, 4 LCA recovers hidden groups in multivariate data of heterogenous populations such that subjects within classes are similar to each other but different form subjects in other classes. It does so by dividing subjects into groups for which the observed variables are unrelated within each class, so-called ‘conditional independence’.5 Given the heterogeneity and multifactorial nature of depression,6 LCA and other multivariate subtyping strategies may yield subtypes with a more homogenous etiology, course of illness or treatment response, than subtyping depressed patients purely on one characteristic, such as with or without anxiety or psychotic features.

In a recent report in Molecular Psychiatry, Milaneschi et al.7 used LCA and identified three classes described as ‘severe typical’ (T), ‘severe atypical’ (A) and ‘moderate’. The two depressed classes T and A differed predominantly with regard to appetite and weight symptoms: most T subjects reported appetite and weight decrease, but almost none reported appetite or weight gain; for A subjects, it was the other way around. Importantly, T and A subtypes did not differ substantially with respect to other depressive symptoms, illustrated by the fact that increased appetite/weight perfectly predicted membership in A (area under the receiver operating characteristic curve (area under curve)=0.99, sensitivity 98.4%, specificity 99.5%), and decreased appetite/weight predicted membership in T very well (area under curve=0.81, sensitivity 87.8%, specificity 72.8%). Both T and A classes are consistent with results from prior LCA-based depression studies. 2, 3, 8

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Acknowledgements

EIF is supported by European Research Council Consolidator Grant no. 647209.

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Authors and Affiliations

  1. Department of Psychiatry, University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, The Netherlands

    H M van Loo, R B K Wanders & K J Wardenaar

  2. Department of Psychiatry, Virginia Commonwealth University, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA

    H M van Loo

  3. University of Leuven, Faculty of Psychology and Educational Sciences, Research Group of Quantitative Psychology and Individual Differences, Leuven, Belgium

    E I Fried

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  1. H M van Loo
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Correspondence to H M van Loo.

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van Loo, H., Wanders, R., Wardenaar, K. et al. Problems with latent class analysis to detect data-driven subtypes of depression. Mol Psychiatry 23, 495–496 (2018). https://doi.org/10.1038/mp.2016.202

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