Multivariate meta-analysis for non-linear and other multi-parameter associations

By Antonio Gasparrini, Ben Armstrong, Michael G. Kenward. Statistics in Medicine, 2012

481 citations recorded by the source corpus.

In this paper, we formalize the application of multivariate meta‐analysis and meta‐regression to synthesize estimates of multi‐parameter associations obtained from different studies. This modelling approach extends the standard two‐stage analysis used to combine results across different sub‐groups or populations. The most straightforward application is for the meta‐analysis of non‐linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta‐analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two‐stage analysis for investigating the non‐linear exposure–response relationship between temperature and non‐accidental mortality using time‐series data from multiple cities. Multivariate meta‐analysis represents a useful analytical tool for studying complex associations through a two‐stage procedure. Copyright © 2012 John Wiley & Sons, Ltd.

Subfields: Applied Psychology, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/66aacd6fd71acce1089597ff1ac0aae3e4065455

Citations in the corpus

• A Meta-Analysis of Relations Between Achievement and Self-Concept (2020, unknown): “Note that the approach that we used in the present meta-analysis (i.e., pooling multiple path analyses; see Becker & Wu, 2007; Gasparrini et al., 2012) had several advantages over the alternative approach, of conducting the path analysis on the pooled correlation matrix.”
• A graphical tool for locating inconsistency in network meta-analyses (2013, unknown): “While multivariate meta-analysis for the aggregation step of studies with the same design can be carried out using standard statistic software [28,37], we provide a preliminary stand-alone R function for the net heat plot available on the website http:// www.”
• Reducing and meta-analysing estimates from distributed lag non-linear models (2013, unknown): “Most of the limitations of DLNMs and multivariate meta-analysis of multi-parameter associations, previously discussed [5,12], identically apply to this framework.”
• Manual for RSiena (2011, unknown): “Still another possibility is offered by the package mvmeta (Gasparrini et al., 2012).”