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Related Experiment Videos

On the relative sample size required for multiple comparisons.

J S Witte1, R C Elston, L R Cardon

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, 2500 MetroHealth Drive, Cleveland OH 44109-1998, USA. witte@darwin.cwru.edu

Statistics in Medicine
|January 29, 2000
PubMed
Summary
This summary is machine-generated.

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Adjusting for multiple comparisons in genetic and epidemiologic research has limited impact on sample size. Increasing the number of tests linearly scales the required sample size with the logarithm of comparisons.

Area of Science:

  • Epidemiology
  • Genetic Research
  • Statistical Methods

Background:

  • Multiple comparisons are frequent in epidemiologic and genetic studies.
  • Appropriate statistical adjustment for these comparisons is debated.
  • Maintaining a consistent experimentwise alpha-level is crucial for study validity.

Purpose of the Study:

  • To investigate the relationship between the number of comparisons and the required sample size.
  • To clarify the impact of increasing comparisons on statistical power.
  • To provide guidance on sample size calculations in studies with multiple testing.

Main Methods:

  • Theoretical analysis of sample size requirements.
  • Mathematical demonstration of the effect of comparison number on alpha-level maintenance.

Related Experiment Videos

  • Formulation of the relationship between sample size and the logarithm of comparisons.

    • Main Results:

    • Large increases in the number of comparisons have a minimal effect on the necessary sample size.
    • The relative sample size increases linearly with the logarithm of the number of comparisons.
    • This finding simplifies sample size considerations in complex research designs.

    Conclusions:

    • The impact of multiple comparisons on sample size is less pronounced than often assumed.
    • Researchers can maintain experimentwise alpha-levels with manageable sample size increases.
    • This research offers a more practical approach to sample size planning in high-throughput research.