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Why are two mistakes not worse than one? A proposal for controlling the expected number of false claims

Thomas Jaki1, Alice Parry1

  • 1Department of Mathematics Statistics, Lancaster University, Lancaster, UK.

Pharmaceutical Statistics
|April 21, 2016
PubMed

Insights

Familywise error rate control in clinical studies may miss errors. This paper proposes using the expected number of false claims (EFC) and a weighted Bonferroni approach to better manage statistical errors in research.

Area of Science:

  • Statistics
  • Clinical Trials
  • Biostatistics

Background:

  • Multiplicity is prevalent in clinical studies.
  • Current standard uses familywise error rate (FWER) for error control.

Purpose of the Study:

  • To highlight limitations of FWER in specific clinical study situations.
  • To introduce the expected number of false claims (EFC) as an alternative error metric.
  • To propose methods for controlling EFC in clinical research.

Main Methods:

  • Demonstrate situations where FWER is insufficient.
  • Introduce and define the expected number of false claims (EFC).
  • Adapt a (weighted) Bonferroni approach for EFC control.

Main Results:

  • FWER control does not account for all errors in certain scenarios.
  • A weighted Bonferroni method can effectively control EFC.
  • Methods for powering studies with EFC for various endpoint types are discussed.

Conclusions:

  • The expected number of false claims (EFC) offers a more comprehensive error control strategy than FWER.
  • A weighted Bonferroni approach provides a practical method for controlling EFC.
  • This framework supports robust statistical decision-making in complex clinical trials.
Keywords:
expected number of false claims (EFC)familywise error ratehierarchical endpointsmultiplicity

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