Investigating data-driven biological subtypes of sychiatric disorders using specification-curve analysis.

ABSTRACT:

Background

Cluster 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.

Methods

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algori

SUBMITTER: Beijers L 

PROVIDER: S-EPMC9069352 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature