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Comparative Study
. 2012 Jan-Feb;37(1):99-105.
doi: 10.1097/AAP.0b013e31823ebc74.

Beyond repeated-measures analysis of variance: advanced statistical methods for the analysis of longitudinal data in anesthesia research

Yan Ma  1 , Madhu Mazumdar, Stavros G Memtsoudis
Affiliations
Comparative Study

Beyond repeated-measures analysis of variance: advanced statistical methods for the analysis of longitudinal data in anesthesia research

Yan Ma et al. Reg Anesth Pain Med. 2012 Jan-Feb.

Abstract

Background and objectives: Research in the field of anesthesiology relies heavily on longitudinal designs for answering questions about long-term efficacy and safety of various anesthetic and pain regimens. Yet, anesthesiology research is lagging in the use of advanced statistical methods for analyzing longitudinal data. The goal of this article was to increase awareness of the advantages of modern statistical methods and promote their use in anesthesia research.

Methods: Here we introduce 2 modern and advanced statistical methods for analyzing longitudinal data: the generalized estimating equations (GEE) and mixed-effects models (MEM). These methods were compared with the conventional repeated-measures analysis of variance (RM-ANOVA) through a clinical example with 2 types of end points (continuous and binary). In addition, we compared GEE and MEM to RM-ANOVA through a simulation study with varying sample sizes, varying number of repeated measures, and scenarios with and without missing data.

Results: In the clinical study, the 3 methods are found to be similar in terms of statistical estimation, whereas the parameter interpretations are somewhat different. The simulation study shows that the methods of GEE and MEM are more efficient in that they are able to achieve higher power with smaller sample size or lower number of repeated measurements in both complete and missing data scenarios.

Conclusions: Based on their advantages over RM-ANOVA, GEE and MEM should be strongly considered for the analysis of longitudinal data. In particular, GEE should be used to explore overall average effects, and MEM should be used when subject-specific effects (in addition to overall average effects) are of primary interest.

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Figures

Figure 1
Figure 1
Underlying Setting for Generation of Simulated Data (St: slope of the treatment line; Sc: slope of the control line).
Figure 2
Figure 2
Relations between truth/falseness of the null hypothesis and outcomes of the test (H0:null hypothesis; H1:alternative hypothesis).
Figure 3
Figure 3
Sample mean curves of CRP with randomly selected individual profiles.
Figure 4
Figure 4
Simulated power analysis under complete data (A=RM-ANOVA, G=GEE, M=MEM).
See this image and copyright information in PMC

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