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Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Unsupervised Clustering for POAG Phenotyping.

Prizka A Puspa1, Uday Pratap Singh Parmar1, Sayuri Sekimitsu1

  • 1Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States.

Investigative Ophthalmology & Visual Science
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

This study identified five distinct phenotypes of primary open-angle glaucoma (POAG) using multimodal data clustering. These findings support better risk stratification and personalized treatment strategies for POAG patients.

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Area of Science:

  • Ophthalmology
  • Glaucoma Research
  • Data Science in Medicine

Background:

  • Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness worldwide.
  • Understanding POAG heterogeneity is crucial for effective management and treatment.
  • Current classification may not fully capture the diverse clinical trajectories of POAG.

Purpose of the Study:

  • To identify and characterize clinically meaningful phenotypic subtypes of POAG.
  • To apply unsupervised clustering algorithms to multimodal clinical data for POAG subtyping.
  • To explore the potential for improved risk stratification and targeted management based on identified phenotypes.

Main Methods:

  • Retrospective cohort study of 4274 eyes from patients with POAG (age ≥40 years).
  • Analysis of 21 clinical features including visual field indices, RNFL thickness, optic nerve head parameters, and IOP.
  • Application of hierarchical clustering, K-means, and fuzzy C-means algorithms, with stability assessed by internal validation and visual field archetypal analysis.

Main Results:

  • Five reproducible POAG phenotypes were identified using k-means and fuzzy C-means clustering.
  • Phenotypes varied in disease severity, progression rates, structural damage (RNFL thickness), and intraocular pressure (IOP) variability.
  • Distinct clusters represented mild stable disease, structural-functional dissociation, aggressive progression, and advanced baseline damage with stability.

Conclusions:

  • Multimodal data clustering effectively identifies distinct POAG phenotypes.
  • These identified phenotypes offer a basis for improved risk stratification in POAG.
  • Targeted management strategies can be developed based on these specific POAG subtypes.