ArticlePsychological medicine2022
Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis.
Article in Psychological medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- SYNERGY-VAE: An explainable generative deep learning framework for discovering depression subgroups from multimodal population health data.PLOS digital health · 2026Article
- An exploratory data-driven stratification of depression based on functional network connectivity and symptom profiles suggests heterogeneous responses to iTBS.BMC psychiatry · 2026Article
- Clustering longitudinal data: comparison of model-based and distance-based approaches using simulated and real-world data in psychiatric research.BMC medical research methodology · 2026Article
- Treatment-resistant depression: role of genetic factors in the perspective of clinical stratification and treatment personalisation.Molecular psychiatry · 2025Review
- The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.Biological psychiatry global open science · 2025Review
- Network analysis of plasma proteomes in affective disorders.Translational psychiatry · 2023Article
- Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity.Translational psychiatry · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.
methodsProteomics data (31 biomarkers) were used from patients (
resultsThe real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.
conclusionSCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.
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