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A non-parametric permutation method for assessing agreement for distance matrix observations.

Jo Røislien1, Eigil Samset

  • 1Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway.

Statistics in Medicine
|August 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel permutation method to assess agreement in medical research using distance matrices. The technique visualizes data structure and compares observed values against permuted means for robust analysis.

Keywords:
agreementdistance matrixhierarchical clusteringpermutation

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Area of Science:

  • Medical research
  • Genetics
  • DNA research
  • Image analysis

Background:

  • Distance matrix data are increasingly prevalent in medical research.
  • Applications span genetics, DNA research, and image analysis.

Purpose of the Study:

  • To propose a non-parametric permutation method for assessing agreement in distance matrix data.
  • To provide a robust statistical test for evaluating similarity within distance matrices.

Main Methods:

  • Utilizing agglomerative hierarchical clustering and dendrograms for data visualization.
  • Employing a permutation test based on random permutations of matrix elements.
  • Comparing within-matrix element sum of squares (WMESS) between observed and permuted means.

Main Results:

  • The permutation method effectively assesses agreement in distance matrices.
  • Hierarchical clustering and dendrograms aid in visualizing matrix structures.
  • Simulations and real magnetic resonance imaging data validated the methodology.

Conclusions:

  • The proposed non-parametric permutation method offers a reliable approach for analyzing distance matrix data in medical research.
  • This technique enhances the understanding of data agreement in fields like genetics and medical imaging.