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• W2783295724 abstract "Despite the popularity of the general linear mixed model for data analysis, power and sample size methods and software are not generally available for commonly used test statistics and reference distributions. Statisticians resort to simulations with homegrown and uncertified programs or rough approximations which are misaligned with the data analysis. For a wide range of designs with longitudinal and clustering features, we provide accurate power and sample size approximations for inference about fixed effects in the linear models we call reversible. We show that under widely applicable conditions, the general linear mixed-model Wald test has noncentral distributions equivalent to well-studied multivariate tests. In turn, exact and approximate power and sample size results for the multivariate Hotelling–Lawley test provide exact and approximate power and sample size results for the mixed-model Wald test. The calculations are easily computed with a free, open-source product that requires only a web browser to use. Commercial software can be used for a smaller range of reversible models. Simple approximations allow accounting for modest amounts of missing data. A real-world example illustrates the methods. Sample size results are presented for a multicenter study on pregnancy. The proposed study, an extension of a funded project, has clustering within clinic. Exchangeability among the participants allows averaging across them to remove the clustering structure. The resulting simplified design is a single-level longitudinal study. Multivariate methods for power provide an approximate sample size. All proofs and inputs for the example are in the supplementary materials (available online)." @default.
• W2783295724 created "2018-01-26" @default.
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• W2783295724 date "2018-06-04" @default.
• W2783295724 modified "2023-10-17" @default.
• W2783295724 title "Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models" @default.
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• W2783295724 doi "https://doi.org/10.1080/00031305.2017.1415972" @default.
• W2783295724 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7009022" @default.
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