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Related Experiment Videos

Adjusted intraclass correlation coefficients for binary data: methods and estimates from a cluster-randomized trial

Lisa N Yelland1, Amy B Salter, Philip Ryan

  • 1Discipline of Public Health, University of Adelaide, Adelaide, Australia. lisa.yelland@adelaide.edu.au

Clinical Trials (London, England)
|February 22, 2011
PubMed
Summary

Accurate intraclass correlation coefficient (ICC) estimation is crucial for cluster-randomized trials. A new method using the log link is recommended for calculating adjusted ICCs when relative risk is the primary outcome, improving sample size calculations.

Related Experiment Videos

Area of Science:

  • Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Intraclass correlation coefficient (ICC) is vital for sample size calculations in cluster-randomized trials.
  • Adjusting for baseline covariates can reduce ICC and sample size.
  • Existing methods for adjusted ICCs use the logit link for odds ratios, but a log link method is needed for relative risks.

Purpose of the Study:

  • To develop and assess a method for calculating adjusted ICCs using the log link.
  • To compare unadjusted and adjusted ICCs derived from a primary care cluster-randomized trial, utilizing both logit and log links.

Main Methods:

  • Proposed two methods for adjusted ICCs with the log link, employing Taylor series expansion and lognormal distribution properties.
  • Evaluated proposed methods through simulation studies.
  • Calculated unadjusted and adjusted ICCs for binary outcomes from the Point of Care Testing (PoCT) Trial using logit and log links.

Main Results:

  • Methods for adjusted ICCs with the log link yielded similar results, except when between-cluster variance was substantial.
  • Unadjusted ICCs in the PoCT Trial varied from 0.001 to 0.048.
  • The effect of covariate adjustment on ICC differed across outcomes and link functions, showing reductions up to 59% and increases up to 89%.

Conclusions:

  • The choice of link function impacts the calculation of adjusted ICCs for binary outcomes.
  • The lognormal distribution-based method is recommended for the log link in cluster-randomized trials focusing on relative risk.
  • This new method enhances the accuracy of sample size estimations when relative risk is the key effect measure.