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Practical p-value adjustment for optimally selected cutpoints

S G Hilsenbeck1, G M Clark

  • 1Department of Medicine, University of Texas Health Science Center at San Antonio 78284-7884, USA.

Statistics in Medicine
|January 15, 1996
PubMed
Summary

This study evaluated p-value adjustment methods for prognostic markers. Empirical permutation (P(emp)) and improved Bonferroni (P(bon)) methods closely matched validation sample rates, while asymptotic (P(acor)) was conservative.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Genomics

Background:

  • Prognostic marker analysis often involves data-derived cutpoints.
  • Selecting appropriate statistical methods for p-value adjustment is crucial for marker validation.

Purpose of the Study:

  • To assess the impact of cutpoint number and marker effect size on p-value adjustment methods.
  • To compare asymptotic (P(acor)), improved Bonferroni (P(bon)), and empirical permutation (P(emp)) adjustment methods.

Main Methods:

  • Simulations were conducted varying the number of cutpoints and effect size.
  • P-value adjustment methods were compared against an independent validation sample (P(vld)).
  • Analysis of a novel breast cancer marker (heat shock protein 70) illustrated the methods.

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Main Results:

  • P(emp) and P(bon) demonstrated rejection rates similar to P(vld).
  • P(acor) showed conservative results, particularly with fewer cutpoints.
  • The findings highlight challenges with data-derived cutpoints.

Conclusions:

  • P(emp) and P(bon) are reliable for adjusting p-values with data-derived cutpoints.
  • P(acor) may be overly conservative in certain scenarios.
  • Emphasizes the necessity of rigorous p-value adjustment in marker studies.