Meta-analysis of repeated measures study designs.
OBJECTIVE
Repeated measures studies are found in many areas of research, particularly in areas of healthcare. There is currently little information available to inform the method of meta-analysis of repeated measures studies so that the structural dependence of the data is appropriately accommodated and the findings are meaningful.


METHODS
Using a published meta-analysis on the impact of diet advice on weight reduction of obese or overweight individuals, we demonstrate possible approaches for repeated measures meta-analysis. These approaches differ in terms of the type of result obtained (e.g. effect at a particular time-point, trend over time, change between time-points) and the data needed for the analysis (e.g. means, regression slope estimates). Some approaches involve violating assumptions of independence in the data structure and so to investigate the impact of this violation a simulation study is carried out.


RESULTS
The different approaches described for the meta-analyses of repeated measures studies can all provide useful effect estimates depending on the question to be addressed by the meta-analysis. However, violation of the independence assumption in some approaches can lead to biased estimates.


CONCLUSIONS
In practice, the methods available to carry out meta-analyses of repeated measures studies will not only depend upon the question of interest, but also on the data available from the primary studies.
Peters
Jaime L
JL
School of Mathematical Sciences, Queensland University of Technology, Australia.
Mengersen
Kerrie L
KL
D001499:Bayes Theorem; D015982:Bias; D003627:Data Interpretation, Statistical; D004038:Diet, Reducing; D019317:Evidence-Based Medicine; D005500:Follow-Up Studies; D006801:Humans; D016014:Linear Models; D015201:Meta-Analysis as Topic; D015999:Multivariate Analysis; D009765:Obesity / Q000178:diet therapy; D017063:Outcome Assessment, Health Care; D010353:Patient Education as Topic; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D012107:Research Design; D012680:Sensitivity and Specificity; D013997:Time Factors; D015431:Weight Loss