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Related Experiment Videos

Dual controls, p-value plots, and the multiple testing issue in carcinogenicity studies.

M R Selwyn1

  • 1Statistics Unlimited, Inc., Auburndale, MA 02166.

Environmental Health Perspectives
|July 1, 1989
PubMed
Summary

Interpreting carcinogenicity study results is challenging due to numerous statistical tests. A new p-value plot method aids in analyzing tumor data and controlling false positive rates effectively.

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Area of Science:

  • Toxicology
  • Biostatistics
  • Carcinogenicity Studies

Background:

  • Interpreting carcinogenicity study findings is complex due to extensive statistical testing.
  • High false positive rates are a concern, leading to suggestions for using multiple control groups.

Purpose of the Study:

  • To present results from carcinogenicity studies utilizing dual control groups.
  • To introduce and demonstrate a novel graphical technique for analyzing and interpreting tumor data.

Main Methods:

  • Analysis of data from two carcinogenicity studies with dual control groups.
  • Development and illustration of a p-value plot graphical technique.
  • Computer simulations to generate p-value plots with and without treatment effects.

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Main Results:

  • Statistically significant differences between identically treated groups occurred frequently, but did not strongly indicate extrabinomial variation in tumor rates.
  • P-value plots visually assessed ensembles of p-values for neoplasm data.
  • Simulated p-value plots differed significantly with and without treatment effects.
  • Decision rules based on p-value plots showed good power for detecting treatment effects and controlling false positive rates.

Conclusions:

  • The p-value plot is a valuable graphical tool for assessing tumor data in carcinogenicity studies.
  • This method aids in visual interpretation and can help control false positive rates.
  • The proposed decision rules offer a balance between detecting treatment effects and maintaining statistical rigor.