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Related Experiment Videos

Estimation of sample sizes in case-control studies with multiple controls per case: dichotomous data.

K J Lui1

  • 1Division of Viral Diseases, Center for Infectious Diseases, Centers for Disease Control, Atlanta, GA 30333.

American Journal of Epidemiology
|May 1, 1988
PubMed
Summary

Calculating exact sample sizes for matched case-control studies is challenging. A new formula offers improved accuracy, especially with small odds ratios and multiple controls per case, outperforming existing approximations.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Sample size calculation for matched case-control studies is complex due to unknown nuisance parameters.
  • Existing approximate formulas by Schlesselman and Stolley, and Taylor rely on assumptions about small exposure differences.

Purpose of the Study:

  • To derive and evaluate an alternative, explicit sample size formula for matched case-control studies.
  • To compare the accuracy of the new formula against existing approximations using Monte Carlo simulations.

Main Methods:

  • Derivation of a new explicit sample size formula.
  • Comparison of three sample size calculation procedures: Schlesselman and Stolley's, Taylor's, and the newly derived formula.
  • Monte Carlo simulations to assess formula accuracy under various conditions.

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Main Results:

  • The newly derived formula is most accurate for small odds ratios (≤4) when using multiple controls per case.
  • Taylor's conservative estimate is recommended for large odds ratios (≥5), except when population exposure prevalence is high (0.9).

Conclusions:

  • The new formula provides a valuable alternative for sample size calculations in matched case-control studies, particularly in specific scenarios.
  • Researchers should consider the odds ratio and number of controls when selecting a sample size formula for optimal accuracy.