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Non-parametric bootstrap confidence intervals for the intraclass correlation coefficient.

Obioha C Ukoumunne1, Anthony C Davison, Martin C Gulliford

  • 1Department of Public Health Sciences, King's College London, 5th Floor, Capital House, 42 Weston Street, London SE1 3QD, UK. obioha.ukoumunne@kcl.ac.uk

Statistics in Medicine
|December 16, 2003
PubMed
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Estimating intraclass correlation (ICC) in cluster randomized trials is crucial. New bootstrap methods improve confidence interval accuracy for ICC, especially with few clusters, enhancing sample size calculations.

Area of Science:

  • Biostatistics
  • Clinical Trials

Background:

  • The intraclass correlation coefficient (ICC) is vital for designing cluster randomized trials.
  • Imprecise ICC estimates from small cluster samples can impact sample size calculations.
  • Quantifying this imprecision is essential for reliable trial design.

Purpose of the Study:

  • To compare various bootstrap confidence interval methods for ICC estimation.
  • To evaluate methods applied to a variance-stabilizing transformation of ICC.
  • To assess performance under different numbers of clusters and ICC values.

Main Methods:

  • Simulation study comparing standard bootstrap intervals (basic, bootstrap-t, percentile, BC, BCA) for ICC.
  • Introduction and evaluation of new methods using bootstrap-t on a variance-stabilizing transformation of ICC.

Related Experiment Videos

  • Data simulated with varying cluster numbers (10-50), ICCs (0.001-0.3), and outcome distributions (normal, non-normal).

    • Main Results:

    • Standard bootstrap methods showed coverage levels substantially below nominal for 10 clusters.
    • Adequate coverage (near 95%) was only achieved with 50 clusters using standard methods.
    • Applying bootstrap-t to the variance-stabilizing transformation of ICC significantly improved coverage, nearing nominal levels.

    Conclusions:

    • Standard bootstrap confidence intervals for ICC are unreliable with few clusters.
    • The proposed bootstrap-t method on a transformed ICC offers improved accuracy and reliability.
    • This enhanced method is valuable for precise sample size determination in cluster randomized trials.