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Related Experiment Videos

Bootstrap confidence intervals for relative risk parameters in affected-sib-pair data.

H J Cordell1, J R Carpenter

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44109, USA. cordell@darwin cwru.edu

Genetic Epidemiology
|January 21, 2000
PubMed
Summary

Estimating sibling relative risk (lambda(s)) in affected-sib-pair studies is crucial for gene discovery. This study finds asymptotic methods are most reliable for accurate confidence intervals, even with small sample sizes.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Genetic epidemiology

Background:

  • Affected-sib-pair (ASP) studies are used to identify susceptibility genes.
  • Estimating locus-specific sibling relative risk (lambda(s)) is key in ASP studies.
  • Confidence intervals for lambda(s) are needed due to variability in point estimates.

Purpose of the Study:

  • To evaluate the reliability of various confidence interval methods for lambda(s) in ASP studies.
  • To identify methods yielding accurate coverage probabilities and short intervals.
  • To compare asymptotic and bootstrap-based confidence interval approaches.

Main Methods:

  • Simulation studies were used to assess confidence interval reliability.
  • Various asymptotic and resampling (bootstrap) methods were evaluated.

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  • Performance was assessed based on coverage probabilities and interval lengths.

    • Main Results:

    • Many popular bootstrap methods performed poorly, showing lower coverage than claimed.
    • Test-inversion, profile-likelihood, and asymptotic methods demonstrated good performance.
    • Asymptotic methods were found to be the most reliable, even in small samples.

    Conclusions:

    • Asymptotic confidence interval methods are recommended for estimating lambda(s) in ASP studies.
    • Careful selection of nuisance parameters is important for some methods.
    • The findings are illustrated with an application to type 1 diabetes loci.