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Logo of bmcmrmBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Medical Research Methodology
 
BMC Med Res Methodol. 2012; 12: 33.
Published online Mar 22, 2012. doi:  10.1186/1471-2288-12-33
PMCID: PMC3414745
On summary measure analysis of linear trend repeated measures data: performance comparison with two competing methods
Mehrdad Vossoughi,1 SMT Ayatollahi,corresponding author1 Mina Towhidi,2 and Farzaneh Ketabchi3
1Department of Biostatistics, Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
2Department of Statistics, College of Sciences, Shiraz University, Shiraz, Iran
3Department of Physiology, Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
corresponding authorCorresponding author.
Mehrdad Vossoughi: vossoughim/at/sums.ac.ir; SMT Ayatollahi: ayatolahim/at/sums.ac.ir; Mina Towhidi: mtowhidi/at/susc.ac.ir; Farzaneh Ketabchi: ketabchiff/at/yahoo.com
Received August 4, 2011; Accepted March 22, 2012.
Abstract
Background
The summary measure approach (SMA) is sometimes the only applicable tool for the analysis of repeated measurements in medical research, especially when the number of measurements is relatively large. This study aimed to describe techniques based on summary measures for the analysis of linear trend repeated measures data and then to compare performances of SMA, linear mixed model (LMM), and unstructured multivariate approach (UMA).
Methods
Practical guidelines based on the least squares regression slope and mean of response over time for each subject were provided to test time, group, and interaction effects. Through Monte Carlo simulation studies, the efficacy of SMA vs. LMM and traditional UMA, under different types of covariance structures, was illustrated. All the methods were also employed to analyze two real data examples.
Results
Based on the simulation and example results, it was found that the SMA completely dominated the traditional UMA and performed convincingly close to the best-fitting LMM in testing all the effects. However, the LMM was not often robust and led to non-sensible results when the covariance structure for errors was misspecified. The results emphasized discarding the UMA which often yielded extremely conservative inferences as to such data.
Conclusions
It was shown that summary measure is a simple, safe and powerful approach in which the loss of efficiency compared to the best-fitting LMM was generally negligible. The SMA is recommended as the first choice to reliably analyze the linear trend data with a moderate to large number of measurements and/or small to moderate sample sizes.
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