Statistical power for cluster analysis

• Published in: BMC bioinformatics Vol. 23; no. 1; pp. 205 - 28
• Main Authors: Dalmaijer, Edwin S., Nord, Camilla L., Astle, Duncan E.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.05.2022; BioMed Central; BMC
• Subjects: Algorithms; Centroids; Cluster Analysis; Clustering; Computational biology; Covariance; Datasets; Dimensionality reduction; Humans; Hypotheses; Latent class analysis; Latent profile analysis; Medical research; Methods; Mixtures; Modelling; Multidimensional scaling; Multivariate analysis; Normal Distribution; Ovaries; Polycystic ovary syndrome; Reduction; Sample Size; Separation; Simulation; Software; Statistical methods; Statistical power; Statistics; Subgroups; United Kingdom; Cluster analysis; Dimensionality reduction; Covariance; Sample size; Simulation; Effect size; Latent profile analysis; Latent class analysis; Statistical power

Cluster algorithms are gaining in popularity in biomedical research due to their compelling ability to identify discrete subgroups in data, and their increasing accessibility in mainstream software. While guidelines exist for algorithm selection and outcome evaluation, there are no firmly establishe...