Statistical tests with accurate size and power for balanced linear mixed models.

Full Abstract

The convenience of linear mixed models for Gaussian data has led to their widespread use. Unfortunately, standard mixed model tests often have greatly inflated test size in small samples. Many applications with correlated outcomes in medical imaging and other fields have simple properties which do not require the generality of a mixed model. Alternately, stating the special cases as a general linear multivariate model allows analysing them with either the univariate or multivariate approach to repeated measures (UNIREP, MULTIREP). Even in small samples, an appropriate UNIREP or MULTIREP test always controls test size and has a good power approximation, in sharp contrast to mixed model tests. Hence, mixed model tests should never be used when one of the UNIREP tests (uncorrected, Huynh-Feldt, Geisser-Greenhouse, Box conservative) or MULTIREP tests (Wilks, Hotelling-Lawley, Roy's, Pillai-Bartlett) apply. Convenient methods give exact power for the uncorrected and Box conservative tests. Simulations demonstrate that new power approximations for all four UNIREP tests eliminate most inaccuracy in existing methods. In turn, free software implements the approximations to give a better choice of sample size. Two repeated measures power analyses illustrate the methods. The examples highlight the advantages of examining the entire response surface of power as a function of sample size, mean differences, and variability.

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Author information

Author/s: Muller, Keith E (KE); Edwards, Lloyd J (LJ); Simpson, Sean L (SL); Taylor, Douglas J (DJ);

Affiliation: Department of Epidemiology and Health Policy Research, Gainesville, FL 32610-0177, USA. Keith.Muller(-atsign-)Biostat.ufl.edu

Grants: 1 R24 HS13353-01 (Agency:AHRQ HHS) ; 5 R24 MD000167-01 (Agency:NCMHD NIH HHS) ; 5 T32 HL007773-09 (Agency:NHLBI NIH HHS) ; 9P30 AI 50410 (Agency:NIAID NIH HHS) ; P01 CA47 982-04 (Agency:NCI NIH HHS) ; R0-1 CA095749-01A1 (Agency:NCI NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2007-Aug; vol 26 (issue 19) : pp 3639-60

Dates: Created 2007/07/30; Completed 2007/09/28; Revised 2007/12/03;

PMID: 17394132, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Diagnostic Imaging - statistics & numerical data
Linear Models

Normal Distribution
United States

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