TY - JOUR
T1 - Effect of imbalance and intracluster correlation coefficient in cluster randomized trials with binary outcomes
AU - Ahn, Chul
AU - Hu, Fan
AU - Skinner, Celette Sugg
N1 - Funding Information:
This work was supported in part by NIH grants UL1 RR024982, R01 CA122330 and R01 HL087768. We thank two anonymous reviewers for their constructive comments and helpful suggestions. We thank Sae Joon Ahn for computational assistance.
PY - 2009/1/15
Y1 - 2009/1/15
N2 - Cluster randomization trials are increasingly popular among healthcare researchers. Intact groups (called 'clusters') of subjects are randomized to receive different interventions, and all subjects within a cluster receive the same intervention. In cluster randomized trials, a cluster is the unit of randomization, and a subject is the unit of analysis. Variation in cluster sizes can affect the sample size estimate or the power of the study. [Guittet, L., Ravaud, P., Giraudeau, B., 2006. Planning a cluster randomized trial with unequal cluster sizes: Practical issues involving continuous outcomes. BMC Medical Research Methodology 6 (17), 1-15] investigated the impact of an imbalance in cluster size on the power of trials with continuous outcomes through simulations. In this paper, we examine the impact of cluster size variation and intracluster correlation on the power of the study for binary outcomes through simulations. Because the sample size formula for cluster randomization trials is based on a large sample approximation, we evaluate the performance of the sample size formula with small sample sizes through simulation. Simulation study findings show that the sample size formula (mp) accounting for unequal cluster sizes yields empirical powers closer to the nominal power than the sample size formula (ma) for the average cluster size method. The differences in sample size estimates and empirical powers between ma and mp get smaller as the imbalance in cluster sizes gets smaller.
AB - Cluster randomization trials are increasingly popular among healthcare researchers. Intact groups (called 'clusters') of subjects are randomized to receive different interventions, and all subjects within a cluster receive the same intervention. In cluster randomized trials, a cluster is the unit of randomization, and a subject is the unit of analysis. Variation in cluster sizes can affect the sample size estimate or the power of the study. [Guittet, L., Ravaud, P., Giraudeau, B., 2006. Planning a cluster randomized trial with unequal cluster sizes: Practical issues involving continuous outcomes. BMC Medical Research Methodology 6 (17), 1-15] investigated the impact of an imbalance in cluster size on the power of trials with continuous outcomes through simulations. In this paper, we examine the impact of cluster size variation and intracluster correlation on the power of the study for binary outcomes through simulations. Because the sample size formula for cluster randomization trials is based on a large sample approximation, we evaluate the performance of the sample size formula with small sample sizes through simulation. Simulation study findings show that the sample size formula (mp) accounting for unequal cluster sizes yields empirical powers closer to the nominal power than the sample size formula (ma) for the average cluster size method. The differences in sample size estimates and empirical powers between ma and mp get smaller as the imbalance in cluster sizes gets smaller.
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U2 - 10.1016/j.csda.2008.09.007
DO - 10.1016/j.csda.2008.09.007
M3 - Article
C2 - 20084091
AN - SCOPUS:56349085893
SN - 0167-9473
VL - 53
SP - 596
EP - 602
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 3
ER -