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Network meta-analysis: a technique to gather evidence from direct and indirect comparisons

Overview of attention for article published in Pharmacy Practice, March 2017
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Title
Network meta-analysis: a technique to gather evidence from direct and indirect comparisons
Published in
Pharmacy Practice, March 2017
DOI 10.18549/pharmpract.2017.01.943
Pubmed ID
Authors

Fernanda S. Tonin, Inajara Rotta, Antonio M. Mendes, Roberto Pontarolo

Abstract

Systematic reviews and pairwise meta-analyses of randomized controlled trials, at the intersection of clinical medicine, epidemiology and statistics, are positioned at the top of evidence-based practice hierarchy. These are important tools to base drugs approval, clinical protocols and guidelines formulation and for decision-making. However, this traditional technique only partially yield information that clinicians, patients and policy-makers need to make informed decisions, since it usually compares only two interventions at the time. In the market, regardless the clinical condition under evaluation, usually many interventions are available and few of them have been studied in head-to-head studies. This scenario precludes conclusions to be drawn from comparisons of all interventions profile (e.g. efficacy and safety). The recent development and introduction of a new technique - usually referred as network meta-analysis, indirect meta-analysis, multiple or mixed treatment comparisons - has allowed the estimation of metrics for all possible comparisons in the same model, simultaneously gathering direct and indirect evidence. Over the last years this statistical tool has matured as technique with models available for all types of raw data, producing different pooled effect measures, using both Frequentist and Bayesian frameworks, with different software packages. However, the conduction, report and interpretation of network meta-analysis still poses multiple challenges that should be carefully considered, especially because this technique inherits all assumptions from pairwise meta-analysis but with increased complexity. Thus, we aim to provide a basic explanation of network meta-analysis conduction, highlighting its risks and benefits for evidence-based practice, including information on statistical methods evolution, assumptions and steps for performing the analysis.

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Mendeley readers

The data shown below were compiled from readership statistics for 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 192 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 18%
Researcher 33 17%
Student > Master 22 11%
Student > Bachelor 17 9%
Other 14 7%
Other 40 21%
Unknown 32 17%
Readers by discipline Count As %
Medicine and Dentistry 64 33%
Pharmacology, Toxicology and Pharmaceutical Science 22 11%
Agricultural and Biological Sciences 9 5%
Biochemistry, Genetics and Molecular Biology 7 4%
Psychology 7 4%
Other 28 15%
Unknown 55 29%