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Correcting for nonrandom ascertainment in generalized linear mixed models (GLMMs), fitted using Gibbs sampling.

Paul R Burton1

  • 1Department of Epidemiology and Public Health and Institute of Genetics, University of Leicester, Leicester, UK. pb51@le.ac.uk

Genetic Epidemiology
|January 1, 2003
PubMed
Summary

Generalized linear mixed models (GLMMs) using Gibbs sampling are extended for genetic epidemiology. A new method corrects for nonrandom sample ascertainment by sampling random effects, improving parameter estimates.

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Area of Science:

  • Genetic Epidemiology
  • Statistical Genetics
  • Biostatistics

Background:

  • Generalized linear mixed models (GLMMs) are valuable for analyzing complex phenotypic data.
  • Standard GLMMs are sensitive to nonrandom ascertainment, leading to biased estimates.
  • Current ascertainment correction methods are computationally intensive for Gibbs sampling.

Purpose of the Study:

  • To develop an efficient ascertainment correction for Gibbs sampling-based GLMMs.
  • To extend the application of GLMMs to nonrandomly ascertained family data.
  • To investigate the implications of ascertainment-adjusted parameter estimates.

Main Methods:

  • Developed a novel ascertainment correction based on sampling random effects instead of integration.
  • Implemented the method within a general-purpose Gibbs sampling framework (e.g., WinBUGS).

Related Experiment Videos

  • Applied the approach to analyze nonrandomly ascertained family data.

    • Main Results:

    • The proposed method provides computationally feasible ascertainment correction for GLMMs.
    • It avoids the prohibitive computational cost of repeated integration in MCMC.
    • The approach yields parameter estimates reflecting the ascertained sample's true distribution.

    Conclusions:

    • This work extends the utility of Gibbs sampling-based GLMMs to nonrandomly ascertained family studies.
    • The novel sampling-based correction enhances the applicability and accuracy of genetic epidemiological analyses.
    • The method offers a practical solution for bias mitigation in genetic studies with ascertainment.