Optimality of equal versus unequal cluster sizes in the context of multilevel intervention studies is examined. A Monte Carlo study is done to examine to what degree asymptotic results on the optimality hold for realistic sample sizes and for different estimation methods. The relative D-criterion, comparing equal versus unequal cluster sizes, almost always exceeded 85%, implying that loss of information due to unequal cluster sizes can be compensated for by increasing the number of clusters by 18%. The simulation results are in line with asymptotic results, showing that, for realistic sample sizes and various estimation methods, the asymptotic results can be used in planning multilevel intervention studies.
|Journal||Communications in Statistics-Simulation and Computation|
|Publication status||Published - 1 Jan 2008|
Candel, M. J. J. M., van Breukelen, G. J. P., Kotova, L., & Berger, M. P. F. (2008). Optimality of equal vs. unequal cluster sizes in multilevel intervention studies: A Monte Carlo study for small sample sizes. Communications in Statistics-Simulation and Computation, 37(1), 222-239. https://doi.org/10.1080/03610910701724052