The effects of excluding treatments from network meta-analyses: survey is a research paper published in BMJ (2013). On theSindex it has a DataRank of 0.614. It has been cited 59 times.
ObjectiveTo examine whether the exclusion of individual treatment comparators, including placebo/no treatment, affects the results of network meta-analysis.DesignSurvey of networks with individual trial data.Data sourcesPubMed and communication with authors of network meta-analyses.Study selection and methodsWe included networks that had five or more treatments, contained at least two closed loops, had at least twice as many studies as treatments, and had trial level data available. Investigators abstracted information about study design, participants, outcomes, network geometry, and the exclusion of eligible treatments.ResultsAmong 18 eligible networks involving 757 randomised controlled trials with 750 possible treatment comparisons, 11 had upfront decided not to consider all treatment comparators and only 10 included placebo/no treatment nodes. In 7/18 networks, there was at least one node whose removal caused a more than 1.10-fold average relative change in the estimated treatments effects, and switches in the top three treatments were observed in 9/18 networks. Removal of placebo/no treatment caused large relative changes of the treatment effects (average change 1.16-3.10-fold) for four of the 10 networks that had originally included placebo/no treatment nodes. Exclusion of current uncommonly used drugs resulted in substantial changes of the treatment effects (average 1.21-fold) in one of three networks on systemic treatments for advanced malignancies.ConclusionExcluding treatments in network meta-analyses sometimes can have important effects on their results and can diminish the usefulness of the research to clinicians if important comparisons are missing.
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Base Score Contribution
0.614
From this paper's citation signal
Citation Network Contribution
0
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Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.