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Salanti, G.

Publications and source records attributed to Salanti, G..

2 recordsLinked to original sources

ASSESSING CONFIDENCE IN THE RESULTS OF NETWORK META-ANALYSIS (CINEMA)

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.

epidemiology↗

Planning a future randomized clinical trial based on a network of relevant past trials

Background: The important role of network meta-analysis of randomized clinical trials in health technology assessment and guideline development is increasingly recognized. This approach has the potential to obtain conclusive results earlier than with new standalone trials or conventional, pairwise meta-analyses.\n\nMethods: Network meta-analyses can also be used to plan future trials. We introduce a four-steps framework to plan a new trial that aims to identify the optimal new design that will update the existing evidence to best serve timely clinical and public health decision making. The new trial designed within this framework does not need to include all competing interventions and comparisons of interest and can contribute direct and indirect evidence to the updated network meta-analysis. We present the method by virtually planning a new trial to compare biologics in rheumatoid arthritis and a new trial to compare two drugs for relapsing-remitting multiple sclerosis.\n\nResults: A trial design based on updating the evidence from a network meta-analysis of relevant previous trials may require a considerably smaller sample size to reach the same conclusion compared with a trial designed and analyzed in isolation. Challenges in the approach include the complexity of the methodology and the need for a coherent network meta-analysis of previous trials with little heterogeneity.\n\nConclusions: When used judiciously, conditional trial design could significantly reduce waste in clinical research.

epidemiology↗