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Comparison of methods for analyzing binary data arising from two-sample twin studies

X J Gao1, N Klar, A Donner

  • 1Department of Clinical Epidemiology, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada.

Genetic Epidemiology
|January 1, 1997
PubMed
Summary
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The goodness-of-fit method is recommended for twin studies comparing genetic and environmental influences. This method demonstrates superior performance in controlling type-one error rates and maintaining statistical power across various conditions.

Area of Science:

  • Biostatistics
  • Human Genetics
  • Behavioral Genetics

Background:

  • Twin studies are crucial for disentangling genetic and environmental contributions to human traits.
  • Comparing monozygotic and dizygotic twins on binary outcomes requires robust statistical methodologies.
  • Existing methods for analyzing binary traits in two-sample twin designs have varying performance characteristics.

Purpose of the Study:

  • To compare the statistical performance of five different methods for analyzing binary traits in two-sample twin studies.
  • To identify the most reliable method for assessing genetic and environmental influences in twin research.
  • To evaluate methods based on type-one error rates and statistical power.

Main Methods:

  • Monte Carlo simulation was employed to assess method performance.

Related Experiment Videos

  • Five statistical procedures were compared: goodness-of-fit (common correlation model), normal approximation (maximum likelihood estimators), odds ratio comparison (Ramakrishnan et al.), generalized estimating equations, and tetrachoric correlation.
  • Performance was evaluated across diverse parameter settings, including sample size, prevalence, and intraclass correlation.

    • Main Results:

    • The goodness-of-fit method, based on the common correlation model, exhibited comparable or superior performance to other methods in terms of type-one error rates and power.
    • The goodness-of-fit approach showed a particular advantage in controlling type-one error rates under conditions of small sample sizes, extreme trait prevalences, or high intraclass correlations.
    • Other methods showed limitations under specific parameter settings.

    Conclusions:

    • The goodness-of-fit method is recommended as the preferred statistical approach for two-sample twin study designs analyzing binary traits.
    • This method offers enhanced reliability, especially in challenging data scenarios common in genetic epidemiology.
    • The findings provide practical guidance for researchers in selecting appropriate statistical tools for twin data analysis.