title: A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes.
abstract: OBJECTIVE
  Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences.
  
  
  METHODS
  We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC.
  
  
  RESULTS
  Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required.
  
  
  CONCLUSIONS
  The methods are developed for equal-sized clusters, whereas cluster size may vary in practice.
  
  
  CONCLUSIONS
  The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.
authors: Department of Biostatistics, UCLA School of Public Health, University of California Los Angeles, Los Angeles, CA, USA. ccrespi@ucla.edu
authors: Catherine M
authors: CM
authors: Crespi
authors: Weng Kee
authors: WK
authors: Wong
authors: Sheng
authors: S
authors: Wu
mesh_terms: D016000:Cluster Analysis; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data