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Related Experiment Videos

A statistical methodology for analyzing co-occurrence data from a large sample.

Hui Cao1, George Hripcsak, Marianthi Markatou

  • 1Department of Biomedical Informatics, 622 West 168th Street, VC-5, Columbia University, New York, NY 10032, USA.

Journal of Biomedical Informatics
|January 2, 2007
PubMed
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Identifying significant associations in large datasets is difficult. This study introduces a novel method combining the volume test and p-value plots to establish a rigorous, non-arbitrary threshold for detecting meaningful item associations.

Area of Science:

  • Statistics
  • Data Mining
  • Bioinformatics

Background:

  • Identifying significant associations in large databases is challenging due to numerous hypotheses and the risk of selecting statistically significant but clinically irrelevant associations.
  • Standard chi-squared (chi2) tests often yield inappropriate associations exceeding traditional significance thresholds (e.g., alpha=.05).
  • Arbitrarily choosing stricter thresholds can lead to the exclusion of potentially important findings.

Purpose of the Study:

  • To develop a more rigorous and less arbitrary method for selecting thresholds to identify meaningful associations in large datasets.
  • To improve the clinical relevance of identified associations by filtering out statistically significant but weak findings.

Main Methods:

  • Combined the volume test of Diaconis and Efron with p-value plots to adjust the p-value of the chi2-statistic.

Related Experiment Videos

  • Utilized a plot of adjusted p-values (1 - p versus N(p)) to identify deviations from linearity, indicating true associations.
  • Employed linear regression for reproducible threshold selection.

    • Main Results:

    • The proposed method successfully identified a threshold for associations.
    • The selected threshold was comparable to thresholds obtained through manual review in experimental settings.
    • The approach offers a systematic way to differentiate true associations from spurious ones.

    Conclusions:

    • The volume test combined with p-value plots provides a robust and reproducible method for threshold selection in association studies.
    • This technique enhances the reliability of identifying clinically significant associations in large databases.
    • The method addresses the limitations of traditional statistical tests in handling multiple comparisons and weak associations.