Power, sample size and sampling costs for clustered data

The data collected in epidemiological or clinical studies are frequently clustered. In such settings, appropriate variance adjustments must be made in order to estimate the sufficient sample size correctly. This paper works through the sample size calculations for clustered data. Importantly, our explicit variance expressions also enable us to optimize the design with respect to the number of clusters and number of subjects; the objective could be either to maximize the power or to minimize the costs with given costs on the clusters and on the individuals. In our approach, units on different levels and treatment groups can have different costs, but the members of the same cluster are assumed to belong to the same treatment group. Design considerations in the health coaching project TERVA are used as motivating examples. R-functions for carrying out the computations presented are provided.