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Reflection on modern methods: calculating a sample size for a repeatability sub-study to correct for measurement

Katy E Morgan1, Sarah Cook1, David A Leon1,2

  • 1Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.

International Journal of Epidemiology
|July 23, 2019
PubMed
Summary

Regression dilution bias, caused by measurement error in continuous exposure variables, can be corrected using repeatability sub-studies. Sample size calculations are crucial for precise bias correction factor estimation, aiding future research planning.

Keywords:
Measurement errorregression dilution biasreliabilityrepeatabilitysample size

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Continuous exposure variables measured with random error in univariable linear regression models cause regression dilution bias.
  • This bias attenuates the observed association between exposure and outcome, making it appear weaker than the true relationship.

Purpose of the Study:

  • To describe a method for calculating the sample size for repeatability sub-studies.
  • To ensure sufficient precision in estimating bias correction factors.
  • To provide practical examples and correction factors for future research.

Main Methods:

  • Utilizing a previously published method for sample size calculation based on anticipated correction factor size and desired precision.
  • Applying the method to cross-sectional studies from the International Project on Cardiovascular Disease in Russia.
  • Calculating correction factors from repeat data from the UK Biobank study.

Main Results:

  • A method for sample size calculation for repeatability sub-studies is presented.
  • The approach is demonstrated using real-world study data.
  • Correction factors derived from UK Biobank data are provided for planning future studies.

Conclusions:

  • Repeatability sub-studies are essential for correcting regression dilution bias.
  • Accurate sample size calculations are vital for precise bias correction.
  • The provided methods and data facilitate planning and conducting future epidemiological research with improved accuracy.