A Simple Approach to Sample Size Calculation for Count Data in Matched Cohort Studies
Pages 321-330
Dexiang Gao, Gary K. Grunwald and Stanley Xu
DOI: http://dx.doi.org/10.6000/1929-6029.2014.03.03.11
Published: 25 August 2014

Abstract: In matched cohort studies exposed and unexposed individuals are matched on certain characteristics to form clusters to reduce potential confounding effects. Data in these studies are clustered and thus dependent due to matching. When the outcome is a Poisson count, specialized methods have been proposed for sample size estimation. However, in practice the variance of the counts often exceeds the mean (i.e. counts are overdispersed), so that Poisson methods don’t apply. We propose a simple approach for calculating statistical power and sample size for clustered Poisson data when the proportion of exposed subjects in a cluster is constant across clusters. We extend the approach to clustered count data with overdispersion, which is common in practice. We evaluate these approaches with simulation studies and apply them to a matched cohort study examining the association of parental depression with health care utilization. Simulation results show that the methods for estimating power and sample size performed reasonably well under the scenarios examined and were robust in the presence of mixed exposure proportions up to 30%.