Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications
作者:
KeithE. Muller,
LisaM. Lavange,
SharonLandesman Ramey,
CraigT. Ramey,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 420
页码: 1209-1226
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10476281
出版商: Taylor & Francis Group
关键词: Analysis of variance;Multivariate linear models;Noncentral distribution;Repeated measures;Sample size determination
数据来源: Taylor
摘要:
Recently developed methods for power analysis expand the options available for study design. We demonstrate how easily the methods can be applied by (1) reviewing their formulation and (2) describing their application in the preparation of a particular grant proposal. The focus is a complex but ubiquitous setting: repeated measures in a longitudinal study. Describing the development of the research proposal allows demonstrating the steps needed to conduct an effective power analysis. Discussion of the example also highlights issues that typically must be considered in designing a study. First, we discuss the motivation for using detailed power calculations, focusing on multivariate methods in particular. Second, we survey available methods for the general linear multivariate model (GLMM) with Gaussian errors and recommend those based onFapproximations. The treatment includes coverage of the multivariate and univariate approaches to repeated measures, MANOVA, ANOVA, multivariate regression, and univariate regression. Third, we describe the design of the power analysis for the example, a longitudinal study of a child's intellectual performance as a function of mother's estimated verbal intelligence. Fourth, we present the results of the power calculations. Fifth, we evaluate the tradeoffs in using reduced designs and tests to simplify power calculations. Finally, we discuss the benefits and costs of power analysis in the practice of statistics. We make three recommendations:1.Align the design and hypothesis of the power analysis with the planned data analysis, as best as practical.2.Embed any power analysis in a defensible sensitivity analysis.3.Have the extent of the power analysis reflect the ethical, scientific, and monetary costs.
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