Multivariate meta-analysis: Potential and promise

By Dan Jackson, R. Riley, Ian R. White. Statistics in Medicine, 2011

517 citations recorded by the source corpus.

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta‐analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta‐analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta‐analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta‐analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta‐analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3850f69122890f119599b73b522375f9ffcac095

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Citations in the corpus

Which Healthy Eating Nudges Work Best? A Meta-Analysis of Field Experiments (2018, unknown): “When asked about what drives their food choices, Americans and Europeans place affective factors, such as taste, well ahead of cognitive factors, such as nutrition or weight control (Glanz et al. 1998, Januszewska et al. 2011).”
netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods (2023, unknown): “Maximum likelihood and restricted maximum likelihood estimates for τ 2 are also available (Jackson, Riley, and White 2011; White 2015).”
A Guide to Conducting a Meta-Analysis with Non-Independent Effect Sizes (2019, background): “Researchers are encouraged to apply a multivariate meta-analysis whenever possible (Jackson et al., 2011) because of the benefits that can be obtained from the correlated effect sizes.”
Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool (2012, background): “2012, 3 80–97 (Jackson et al., 2011; Mavridis and Salanti, 2012).”
Multivariate meta-analysis for non-linear and other multi-parameter associations (2012, unknown): “This contribution originates from a commentary published in this journal [10] to the paper by Jackson and collaborators on multivariate meta-analysis [11].”
Graphical Tools for Network Meta-Analysis in STATA (2013, unknown): “An appropriate analysis of multiple outcomes should be undertaken using multivariate methods to account for the dependency between outcomes [45,46].”
GetReal in network meta‐analysis: a review of the methodology (2016, background): “On the other hand, a joint meta-analysis of all outcomes, which incorporates possible correlations, can increase precision by ‘borrowing of strength’ across outcomes and may reduce the impact of outcome reporting bias (Jackson et al., 2011; Kirkham et al., 2012).”
Reducing and meta-analysing estimates from distributed lag non-linear models (2013, methodology): “This problem occurs in likelihood-based and method of moments estimation procedures of multivariate meta-analysis, as these estimators truncate the between-study correlations on the boundary of their parameter space [16,19,23].”
Meta‐analysis using individual participant data: one‐stage and two‐stage approaches, and why they may differ (2016, methodology): “However, a standard two-stage approach (as outlined in Section 2) does not account for such correlations unless a multivariate meta-analysis model is used to jointly synthesise all parameters, whilst accounting for their within-study, and possibly between-study, correlations [55,83].”
robumeta: An R-package for robust variance estimation in meta-analysis (2015, background): “RVE holds promise for alleviating the problems introduced by unknown within-study correlations in meta-analysis(Jackson et al. 2011).”
A graphical tool for locating inconsistency in network meta-analyses (2013, methodology): “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://…”