Advanced methods in meta‐analysis: multivariate approach and meta‐regression

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Multivariate meta‐analysis: the effect of ignoring within‐study correlation (2009, methodology): “For instance, it allows us to describe the multivariate relationship between end points (Van Houwelingen et al., 2002), to obtain joint confidence regions (Reitsma et al., 2005), to model, test or make predictions from their association (Daniels and Hughes, 1997) and to estimate some function of…”
Tobacco smoking and cancer: A meta‐analysis (2008, methodology): “When several measures of RR were given for a single study, we used random effects models, even in absence of heterogeneity, including the 2 sources of variation (within and between studies), to take into account correlation within study.(10)”
Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution (2016, methodology): “Fixed-effect meta-analysis combines estimated effect sizes and their standard errors from different studies to construct pooled estimates resulting in higher precision of effect-size estimates and hence superior statistical power [35, 36].”
Confidence intervals for the amount of heterogeneity in meta‐analysis (2007, background): “When information about the relevant characteristics of the studies are available, then it may be possible to account for the heterogeneity using appropriately formulated meta-regression models [2, 3, 6].”