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Move Over ANOVA:  Progress in Analyzing Repeated-Measures Data andIts Reflection in Papers Published in the Archives of General Psychiatry

Ralitza Gueorguieva, PhD; John H. Krystal, MD
Arch Gen Psychiatry. 2004;61(3):310-317. doi:10.1001/archpsyc.61.3.310.
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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.

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Figure 1.

Hypothetical example of a randomintercept/random slope model. The 6 gray lines correspond to responses overtime of 6 hypothetical subjects. The individuals differ at baseline and inthe rate of change over time.

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Figure 2.

Mean and SD for 2 groups of subjectsreceiving 2 different treatments in a hypothetical repeated-measures example.

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Figure 3.

Average Lehman Quality of LifeIndices of Finance Satisfaction (QLI-Finance) for the placebo and short- andlong-term naltrexone treatment arms of the naltrexone clinical trial. No significanttreatment × time effect was observed using traditional and mixed-modelapproaches.

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