期刊
STATISTICS IN MEDICINE
卷 22, 期 16, 页码 2535-2551出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/sim.1341
关键词
multivariate analysis of covariance; univariate approach to repeated measures; Hotelling-Lawley trace; Huynh-Feldt test; Geisser-Greenhouse test; Box test
类别
资金
- AHRQ HHS [T32 HS 00058-04] Funding Source: Medline
- NCI NIH HHS [K07 CA088811-01A1, K07 CA088811, P01 CA 47982-04] Funding Source: Medline
The analysis of covariance provides a common approach to adjusting for a baseline covariate in medical research. With Gaussian errors, adding random covariates does not change either the theory or the computations of general linear model data analysis. However, adding random covariates does change the theory and computation of power analysis. Many data analysts fail to fully account for this complication in planning a study. We present our results in five parts. (i) A review of published results helps document the importance of the problem and the limitations of available methods. (ii) A taxonomy for general linear multivariate models and hypotheses allows identifying a particular problem. (iii) We describe how random covariates introduce the need to consider quantiles and conditional values of power. (iv) We provide new exact and approximate methods for power analysis of a range of multivariate models with a Gaussian baseline covariate, for both small and large samples. The new results apply to the Hotelling-Lawley test and the four tests in the univariate approach to repeated measures (unadjusted, Huynh-Feldt, Geisser-Greenhouse, Box). The techniques allow rapid calculation and an interactive, graphical approach to sample size choice. (v) Calculating power for a clinical trial of a treatment for increasing bone density illustrates the new methods. We particularly recommend using quantile power with a new Satterthwaite-style approximation. Copyright (C) 2003 John Wiley Sons, Ltd.
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