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Related Experiment Videos

Large tables.

G R Law1, D R Cox, N E Machonochie

  • 1Leukaemia Research Fund Centre for Clinical Epidemiology, University of Leeds, Leeds, UK. g.r.law@leeds.ac.uk

Biostatistics (Oxford, England)
|August 23, 2003
PubMed
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This study introduces an empirical Bayesian method to analyze large contingency tables, reducing spurious associations. The approach helps identify true occupational cancer risks by shrinking estimates and using visual aids for outlier detection.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Occupational Health

Background:

  • Analyzing large contingency tables presents challenges due to multiple comparisons and the generation of chance associations.
  • Identifying genuine associations requires robust statistical methods to mitigate false positives.

Purpose of the Study:

  • To develop and illustrate a statistical approach for analyzing large contingency tables, specifically addressing multiple comparisons.
  • To provide a method for shrinking association estimates towards a global mean, thereby reducing spurious findings.
  • To enhance the visual detection of outliers and significant associations using ordered normal plots with simulation-derived guide rails.

Main Methods:

  • An empirical Bayesian approach was employed to derive shrunken estimates of association.

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  • Ordered normal plots were utilized for visualizing these estimates.
  • Simulation-derived 'guide rails' were added to the plots to aid in outlier detection.

    • Main Results:

    • The empirical Bayesian method effectively shrinks estimates, reducing the impact of chance associations in large contingency tables.
    • Ordered normal plots with guide rails facilitate the visual identification of significant outliers, representing potential true associations.
    • The methodology was successfully illustrated using a dataset of cancer registrations and occupations in England and Wales (1971-90).

    Conclusions:

    • The proposed empirical Bayesian approach offers a robust method for analyzing large contingency tables in epidemiological studies.
    • This technique improves the reliability of identifying genuine associations by controlling for multiple comparisons.
    • The visual tools enhance the interpretability of results, aiding in the detection of occupational cancer risks.