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Multiple comparison procedures updated

J Ludbrook1

  • 1University of Melbourne Department of Surgery, Royal Melbourne Hospital, Parkville, Victoria, Australia. johnludbrook@bigpond.com

Clinical and Experimental Pharmacology & Physiology
|January 15, 1999
PubMed
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Biomedical researchers often commit statistical errors by testing multiple hypotheses without correction, increasing false positives. This review highlights step-wise multiple comparison procedures (MCP) like Holm

Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • A prevalent statistical flaw in biomedical research involves testing multiple null hypotheses from a single experiment without adjusting for the increased risk of Type 1 error (false positives).
  • Multiple Comparison Procedures (MCP) are essential for mitigating this risk, particularly when analyzing pairwise contrasts, a common practice among biomedical investigators.

Purpose of the Study:

  • To review and evaluate various MCPs, focusing on their effectiveness in controlling family-wise Type 1 error rates.
  • To introduce and commend a new class of step-wise MCPs that offer improved power and versatility over traditional one-step methods.

Main Methods:

  • Review of existing literature on MCPs, including one-step procedures (Bonferroni, Sidák, Tukey-Kramer, Dunnett).
  • Evaluation of a newer class of step-wise MCPs, emphasizing their properties and application.

Related Experiment Videos

  • Focus on Holm's step-down procedures as a recommended example of step-wise MCPs.

    • Main Results:

    • One-step MCPs based on Bonferroni or Sidák inequalities effectively control family-wise Type 1 error.
    • Step-wise MCPs, derived from Bonferroni or Sidák inequalities, offer comparable error control but with increased statistical power, especially for related hypotheses.
    • Step-wise MCPs are versatile, applicable to continuous, ordinal, and categorical data, and can be performed manually.

    Conclusions:

    • Holm's step-down procedures are recommended for their accuracy, power, versatility, and simplicity in adjusting for multiple comparisons.
    • Despite advancements in MCPs, researchers are advised to design experiments to test a single global hypothesis to minimize statistical flaws.