Efthimiou, O;
White, IR;
(2019)
The dark side of the force: multiplicity issues in network meta-analysis and how to address them.
Research Synthesis Methods
, 11
(1)
pp. 105-122.
10.1002/jrsm.1377.
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Abstract
Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, e.g. when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multi-level Bayesian modeling, where treatment effects are modeled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta-analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta-analysis model.
Type: | Article |
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Title: | The dark side of the force: multiplicity issues in network meta-analysis and how to address them |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1002/jrsm.1377 |
Publisher version: | https://doi.org/10.1002/jrsm.1377 |
Language: | English |
Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10081521 |
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