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Re: st: Re: XTmixed vs repeated measures anova
.
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:
http://brn.sagepub.com/cgi/content/refs/6/2/151
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, [email protected]
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]
PMID:12941535
We also had a recent training session on this at the Vanderbilt
Kennedy Center by Warren Lamber, and you can download the materials
from:
http://kc.vanderbilt.edu/quant/Seminar/schedule.htm
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.
Manks,
Janet
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