Abstract
Studies of individuals sampled in unbalanced clusters have become common in health services and epidemiological research, but available tools for power/sample size estimation and optimal design are currently limited. This paper presents and illustrates power estimation formulas for t-test comparisons of effect of an exposure at the cluster level on continuous outcomes in unbalanced studies with unequal numbers of clusters and/or unequal numbers of subjects per cluster in each exposure arm. Iterative application of these power formulas obtains minimal sample size needed and/or minimal detectable difference. SAS subroutines to implement these algorithms are given in the Appendices. When feasible, power is optimized by having the same number of clusters in each arm k A =k B and (irrespective of numbers of clusters in each arm) the same total number of subjects in each arm n A k A =n B k B . Cost beneficial upper limits for numbers of subjects per cluster may be approximately (5/ρ) −5 or less where ρ is the intraclass correlation. The methods presented here for simple cluster designs may be extended to some settings involving complex hierarchical weighted cluster samples.
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Hoover, D.R. Power for T-test comparisons of unbalanced cluster exposure studies. J Urban Health 79, 278–294 (2002). https://doi.org/10.1093/jurban/79.2.278
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DOI: https://doi.org/10.1093/jurban/79.2.278