What is it about?
Network meta-analysis (NMA) is a common approach to synthesize the efficacy of multiple therapeutic interventions, and to compare their relative efficacy. Most NMA are based on aggregate data, that is, published summary estimates of relative treatment effect (e.g. as identified through a systematic literature search). It is, however, also possible to conduct an NMA with the raw individual participant data (IPD) from each study. We we set out to explore common challenges and potential advantages of NMA based on IPD.
Why is it important?
Our findings show that access to raw participant data may help to resolve inconsistency and reduce heterogeneity in network meta-analysis. This, in turn, may enhance the validity and clinical usefulness of summary estimates of comparative treatment effects.
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This page is a summary of: An overview of methods for network meta-analysis using individual participant data: when do benefits arise?, Statistical Methods in Medical Research, August 2016, SAGE Publications, DOI: 10.1177/0962280216660741.
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