%0 Journal Article %1 Nikolakopoulou2019 %A Nikolakopoulou, Adriani %A Higgins, Julian P T %A Papakonstantinou, Theodore %A Chaimani, Anna %A Giovane, Cinzia Del %A Egger, Matthias %A Salanti, Georgia %D 2019 %J bioRxiv %K imported %P 597047 %R 10.1101/597047 %T Assessing Confidence in the Results of Network Meta-Analysis (Cinema) %U http://biorxiv.org/content/early/2019/04/05/597047.abstract %X Evaluation of the credibility of results from a meta-analysis has become an intrinsic part of the evidence synthesis process. We present a methodological framework to evaluate Confidence In the results from Network Meta-Analysis (CINeMA) when multiple interventions are compared. CINeMA considers six domains and we outline the methods used to form judgements about within-study bias, across-studies bias, indirectness, imprecision, heterogeneity and incoherence. Key to judgements about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The use of contribution matrix allows the semi-automation of the process, implemented in a freely available web application (cinema.ispm.ch). In evaluating imprecision, heterogeneity and inconsistency we consider the impact of these components of variability in forming clinical decisions. Via three examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgements, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks, like a network involving 18 different antidepressant drugs.

@article{Nikolakopoulou2019, abstract = {Evaluation of the credibility of results from a meta-analysis has become an intrinsic part of the evidence synthesis process. We present a methodological framework to evaluate Confidence In the results from Network Meta-Analysis (CINeMA) when multiple interventions are compared. CINeMA considers six domains and we outline the methods used to form judgements about within-study bias, across-studies bias, indirectness, imprecision, heterogeneity and incoherence. Key to judgements about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The use of contribution matrix allows the semi-automation of the process, implemented in a freely available web application (cinema.ispm.ch). In evaluating imprecision, heterogeneity and inconsistency we consider the impact of these components of variability in forming clinical decisions. Via three examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgements, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks, like a network involving 18 different antidepressant drugs.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Nikolakopoulou, Adriani and Higgins, Julian P T and Papakonstantinou, Theodore and Chaimani, Anna and Giovane, Cinzia Del and Egger, Matthias and Salanti, Georgia}, biburl = {https://www.bibsonomy.org/bibtex/2d0d1060f0d107d6e158b6a69ec5f07e6/jepcastel}, doi = {10.1101/597047}, interhash = {c2bf574ccd7ed776e70542cab6bafdfe}, intrahash = {d0d1060f0d107d6e158b6a69ec5f07e6}, journal = {bioRxiv}, keywords = {imported}, month = {1}, note = {Network meta-analysis; Critical appraisal; Online}, pages = 597047, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Assessing Confidence in the Results of Network Meta-Analysis (Cinema)}, url = {http://biorxiv.org/content/early/2019/04/05/597047.abstract}, year = 2019 }