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Re: st: Re: XTmixed vs repeated measures anova

From   David Airey <david.airey@Vanderbilt.Edu>
Subject   Re: st: Re: XTmixed vs repeated measures anova
Date   Tue, 13 May 2008 11:09:49 -0500


Do you get the same answers? They can be equivalent, but generally I have read the xtmixed approach is favorable when you have longitudinal data (3 or more data per person or experimental unit).

There are some features in xtmixed that are missing relative to other software packages for mixed model analysis of repeated measures data. See Linear Mixed Models: A Practical Guide Using Statistical Software, available from the Stata bookstore.

A couple references are below, but these do not directly answer your question:

Biological Research For Nursing, Vol. 6, No. 2, 151-157 (2004)
DOI: 10.1177/1099800404267682
© 2004 SAGE Publications
A Comparison of the General Linear Mixed Model and Repeated Measures ANOVA Using a Dataset with Multiple Missing Data PointsCharlene Krueger, PhD, RN
University of Florida College of Nursing, Gainesville,

Lili Tian, PhD

University of Florida College of Medicine

Longitudinal methods are the methods of choice for researchers who view their phenomena of interest as dynamic. Although statistical methods have remained largely fixed in a linear view of biology and behavior, more recent methods, such as the general linear mixed model (mixed model), can be used to analyze dynamic phenomena that are often of interest to nurses. Two strengths of the mixed model are (1) the ability to accommodate missing data points often encountered in longitudinal datasets and (2) the ability to model nonlinear, individual characteristics. The purpose of this article is to demonstrate the advantages of using themixed model for analyzing nonlinear, longitudinal datasets with multiple missing data points by comparing the mixed model to the widely used repeated measures ANOVA using an experimental set of data. The decision-making steps in analyzing the data using both the mixed model and the repeated measures ANOVA are described.

Key Words: longitudinal methods • general linear mixed model • dynamic • statistical analyses

Arch Gen Psychiatry. 2004 Mar;61(3):310-7.

Move over ANOVA: progress in analyzing repeated-measures data and its reflection
in papers published in the Archives of General Psychiatry.

Gueorguieva R, Krystal JH.

Department of Epidemiology and Public Health, Yale University School of Medicine,
New Haven, Connecticut, USA.

BACKGROUND: The analysis of repeated-measures data presents challenges to
investigators and is a topic for ongoing discussion in the Archives of General
Psychiatry. Traditional methods of statistical analysis (end-point analysis and
univariate and multivariate repeated-measures analysis of variance [rANOVA and
rMANOVA, respectively]) have known disadvantages. More sophisticated
mixed-effects models provide flexibility, and recently developed software makes
them available to researchers. OBJECTIVES: To review methods for
repeated-measures analysis and discuss advantages and potential misuses of
mixed-effects models. Also, to assess the extent of the shift from traditional to
mixed-effects approaches in published reports in the Archives of General
Psychiatry. DATA SOURCES: The Archives of General Psychiatry from 1989 through
2001, and the Department of Veterans Affairs Cooperative Study 425. STUDY
SELECTION: Studies with a repeated-measures design, at least 2 groups, and a
continuous response variable. DATA EXTRACTION: The first author ranked the
studies according to the most advanced statistical method used in the following
order: mixed-effects model, rMANOVA, rANOVA, and end-point analysis. DATA
SYNTHESIS: The use of mixed-effects models has substantially increased during the
last 10 years. In 2001, 30% of clinical trials reported in the Archives of
General Psychiatry used mixed-effects analysis. CONCLUSIONS: Repeated- measures
ANOVAs continue to be used widely for the analysis of repeated- measures data,
despite risks to interpretation. Mixed-effects models use all available data, can
properly account for correlation between repeated measurements on the same
subject, have greater flexibility to model time effects, and can handle missing
data more appropriately. Their flexibility makes them the preferred choice for
the analysis of repeated-measures data.

PMID: 14993119 [PubMed - indexed for MEDLINE]

Related Links

A comparison of the general linear mixed model and repeated measures ANOVA using
a dataset with multiple missing data points. [Biol Res Nurs. 2004] PMID:15388912

Assessing and interpreting treatment effects in longitudinal clinical trials with
missing data. [Biol Psychiatry. 2003] PMID:12706959

Statistical models for analyzing repeated quality measurements of horticultural
products. Model evaluations and practical example. [Math Biosci. 2003]

We also had a recent training session on this at the Vanderbilt Kennedy Center by Warren Lamber, and you can download the materials from:

On May 13, 2008, at 5:44 AM, Janet Hill wrote:

Following recent threads on Statalist I have been
trying to teach myself longitudinal modelling using
Rabe-Hesketh and Skrondal's excellent book. However I
am a little confused on the choice of xtmixed for the
analysis of repeated measures - in particular is there
any advantage to using it over standard repeated
measures anova where I can use contrasts to evaluate
effects, or am I missing something blindingly obvious.

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