Abstract:
Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined
time points. The most common approach involves performing separate univariate metaanalyses
at individual time points. This simplistic approach ignores dependence between
longitudinal effect sizes, which might result in less precise parameter estimates. In this
paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast
different covariance structures for dependence between effect sizes, both within and
between studies. We propose new combinations of covariance structures for the dependence
between effect size and utilize a practical example involving meta-analysis of 17 trials
comparing postoperative treatments for a type of cancer, where survival is measured at
6, 12, 18 and 24 months post randomization. Although the results from this particular data
set show the benefit of accounting for within-study serial correlation between effect sizes,
simulations are required to confirm these results.
