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Related Experiment Videos

Empirical-Bayes adjustments for multiple comparisons are sometimes useful.

S Greenland1, J M Robins

  • 1Department of Epidemiology, UCLA School of Public Health 90024-1772.

Epidemiology (Cambridge, Mass.)
|July 1, 1991
PubMed
Summary

While Rothman advises against multiple comparison adjustments for data interpretation, this study suggests Bayes and empirical-Bayes methods offer superior decision-making frameworks when statistical inference is secondary to actionable outcomes.

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Area of Science:

  • Statistical analysis
  • Epidemiology
  • Decision theory

Background:

  • Rothman's 1990 recommendation against multiple comparison adjustments is based on the assumption of data interpretation as the sole objective.
  • Frequentist interpretations of summary statistics may be abandoned when focusing on decision-making.

Purpose of the Study:

  • To explore the utility of Bayesian and empirical-Bayes adjustments in statistical analysis.
  • To evaluate alternative approaches to multiple comparison adjustments when the primary goal is decision-making.

Main Methods:

  • The study discusses the theoretical underpinnings of Bayesian and empirical-Bayes methods.
  • It contrasts these approaches with conventional procedures in the context of multiple comparisons.

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Main Results:

  • Bayes and empirical-Bayes adjustments can provide a better basis for decisions compared to conventional procedures.
  • These methods are particularly relevant when the primary goal of data analysis is to reach a set of decisions.

Conclusions:

  • The recommendation against multiple comparison adjustments is context-dependent.
  • Bayesian approaches offer a valuable alternative for statistical decision-making in specific analytical scenarios.