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Related Experiment Videos

Intraclass correlation coefficients and bootstrap methods of hierarchical binary outcomes.

Shiquan Ren1, Shuqin Yang, Shenghan Lai

  • 1Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

Statistics in Medicine
|January 18, 2006
PubMed
Summary

This study introduces a robust bootstrap method for estimating intraclass correlations in binomial outcomes from multistage cluster sampling. The non-parametric bootstrap approach proved more effective for analyzing complex survey data.

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Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Intraclass correlation coefficients (ICCs) measure agreement between quantitative measurements.
  • Estimating ICCs for binomial outcomes in multistage cluster sampling is challenging due to complex statistical distributions.
  • Existing methods lack readily available solutions for this specific scenario.

Purpose of the Study:

  • To propose and evaluate a bootstrap method for estimating the standard error and confidence interval of ICCs for binomial outcomes in multistage cluster sampling.
  • To compare the performance of parametric and non-parametric bootstrap methods in this context.
  • To investigate the effectiveness of non-parametric bootstrap sampling at different hierarchical levels.

Main Methods:

  • Utilized a multilevel generalized linear model framework.

Related Experiment Videos

  • Implemented both parametric and non-parametric bootstrap methods for estimation.
  • Conducted simulation studies and analyzed survey data from China to assess method robustness and effectiveness.

    • Main Results:

    • The non-parametric bootstrap method demonstrated greater robustness compared to the parametric bootstrap.
    • Effectiveness of non-parametric bootstrap sampling was found to be higher at the highest sampling level.
    • The proposed method provides a viable approach for analyzing ICCs in complex survey designs.

    Conclusions:

    • The non-parametric bootstrap method is a reliable tool for estimating ICCs with binomial outcomes in multistage cluster sampling.
    • The findings highlight the importance of sampling level in bootstrap effectiveness for complex survey data.
    • This research offers practical solutions for statistical analysis in complex survey designs.