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Effect of Imbalance and Intracluster Correlation Coefficient in Cluster Randomized Trials with Binary Outcomes
Chul Ahn1, Fan Hu, Celette Sugg Skinner
1Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX.
Computational Statistics & Data Analysis
|January 20, 2010
Summary
Cluster randomization trials require careful sample size estimation. This study found that accounting for unequal cluster sizes improves power calculations for binary outcomes compared to using average cluster size, especially with significant size imbalances.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Health Services Research
Background:
- Cluster randomized trials (CRTs) are widely used in healthcare research, with clusters (groups of subjects) randomized to interventions.
- CRTs face challenges in sample size estimation due to variation in cluster sizes and intracluster correlation.
- Existing sample size formulas for CRTs often rely on large sample approximations.
Purpose of the Study:
- To investigate the impact of cluster size variation and intracluster correlation on statistical power for binary outcomes in CRTs.
- To evaluate the performance of CRT sample size formulas with small sample sizes.
- To compare the accuracy of sample size formulas that account for unequal cluster sizes versus those using average cluster size.
Main Methods:
- The study employed simulation methods to assess power and sample size calculations.
- Simulations were conducted for binary outcomes, considering varying cluster sizes and intracluster correlation.
- The performance of two sample size formulas, m(p) (unequal sizes) and m(a) (average size), was evaluated.
Main Results:
- The sample size formula accounting for unequal cluster sizes (m(p)) provided empirical powers closer to the nominal power than the average cluster size method (m(a)).
- The discrepancy between m(a) and m(p) in sample size estimates and empirical powers decreased as cluster size imbalance lessened.
- The findings highlight the importance of considering cluster size variation for accurate power estimation in CRTs.
Conclusions:
- For cluster randomized trials with binary outcomes, sample size formulas that incorporate unequal cluster sizes are more accurate than those using average cluster size.
- Accurate sample size estimation in CRTs is crucial for achieving desired statistical power, particularly when cluster sizes vary significantly.
- The study underscores the need for refined sample size methodologies in cluster randomized trials to enhance research efficiency and validity.
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