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A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster
Melissa C Braschel1, Ivana Svec2, Gerarda A Darlington2
1Department of Mathematics & Statistics, University of Guelph, Guelph, ON, Canada mbrasche@uoguelph.ca.
Clinical Trials (London, England)
|September 30, 2015
Summary
A new method for calculating confidence intervals for the intraclass correlation coefficient in binary outcome trials with few large clusters improves accuracy. This approach enhances sample size estimation for community intervention trials.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Public Health Research
Background:
- Investigators often use point estimates of the intraclass correlation coefficient (ICC) instead of confidence intervals for sample size calculations in cluster randomized trials.
- Existing confidence interval methods for ICC are limited for binary outcomes in community-based trials with few large clusters.
- Accurate ICC estimation is crucial for determining appropriate sample sizes in newly planned trials.
Purpose of the Study:
- To evaluate existing confidence interval methods for ICC with binary outcomes in community intervention trials.
- To introduce and assess a novel approach for constructing ICC confidence intervals by dividing clusters into sub-clusters.
Main Methods:
- Monte Carlo simulations were employed to assess the width and coverage of various ICC confidence interval methods.
- Methods evaluated include Smith's approximation, an inverted modified Wald test, and bootstrap-t intervals.
- A new ad hoc approach involved dividing clusters into smaller sub-clusters and reapplying existing methods.
Main Results:
- Existing ICC confidence interval methods showed poor coverage for binary outcomes in small, large-cluster trials.
- The novel approach, combining sub-clustering with Smith's method, yielded nominal or near-nominal coverage for small ICC values (<0.05).
- This improved coverage is particularly relevant for most community intervention trials where ICC is typically small.
Conclusions:
- Dividing clusters into sub-clusters (e.g., groups of 5) and applying Smith's method is recommended for ICC confidence intervals in binary outcome trials.
- This method provides reliable coverage, aiding investigators in understanding the uncertainty of ICC point estimates.
- Improved ICC confidence interval estimation facilitates more accurate sample size determination for community-based cluster randomized trials.
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