 Γεια σας, είμαι ο Θοδωρίς Παπακωνσταντίνο, θα Περνήσω με τη Μετισία Σοσιακή Βαρνά, του Μετισία Βανή και της Τύγκο Μαρκίνου. Θα διαφαίνω για την συζήτηση της μεταναλικής υποδιοκοπίας με τα κυσημερίοδο της μεταναλίας. Θα αρχίσω με την συζήτηση της μεταναλικής υποδιοκοπίας της Perwise. Σης υποδιοκοπίας της eyesight, αχιλώπουμε δύliers. Εκεί, υποδιοκοπιο A vs. υποδιο B. We do that by synthesizing various studies examining this comparison. Each study represented here with dotted lines is weighted by the inverse of its variance and the summary effect, theta hat, is the weighted sum of individual study effects. The contribution of each study is its weight normalized by the sum of the weights and it sums up to one. In network meta-analysis we can compare more than two treatments, not only A versus B as in the previous example. This means that not only can we borrow strength from indirect comparisons but also we can compare all pairs of treatments, even those that have not been directly studied, as is the case for B versus C. Network meta-analysis can be performed as a two-stage regression model. In the first stage we perform classical pairwise meta-analysis to all comparisons with direct studies. And in the second stage we combine the direct summary effects to derive the network summary effects, theta superscript n here. The matrix that maps direct to network effects is called H matrix. The H matrix is calculated exclusively by the variances of studies and resembles the hat matrix of a linear regression model. Each row of the H matrix refers to a single network effect and columns refer to direct comparisons. For the A, B network comparison, the row of interest is the first one. So the task now is to turn the elements of the H matrix into contributions. The contribution of studies having directly compared A versus B or else direct contribution is by definition equal to the respective element of the H depicted here with bold font. This excludes the solution of simply normalizing the H elements given that the sum of the elements of a row do not generally adapt to one. Our approach was motivated by the observation of Keening et al that H row elements represent flow from A to B in the respective flow graph. And the total flow is always one. So assigning contribution becomes the task of partitioning or decomposing the aforementioned flow. We break up the flow in the graph into number of streams of constant flow where the contribution of a stream is equal to its flow. The stream is therefore defined by the path it follows its flow. For example, in this figure the total flow is divided into the upper, orange and lower blue stream. In order to calculate the contribution of studies from streams we first break up the contribution of a stream into its constituent comparisons. We then assign a contribution of a comparison to be that of a stream divided by its length. So for the example here we divide the flow ACDB by 3. If a comparison takes part in multiple streams we have to add the contribution from each stream in order to get its total contribution. We then calculate the contribution of the studies included in a comparison by the use of the perwise metanalysis weights. Through the collaboration with the Institute of Medical Biometry and Statistics of the University of Freiburg the contribution matrix will be included in an upcoming version of net meta. This will be as simple as calling the net contrib function on the net meta object as you can see in the good dataset example here. Its row of the matrix is a network comparison and its column is a direct comparison. I would also like to introduce the NMADB grant packets which gives access to the database of network meta-analysis curated by ISPM Bern, Switzerland and the University of Ioannina Greece. Through NMADB you gain access to 213 published networks a number which is expected to rise since the database is being currently updated. The plot here is an application of the contribution matrix to the database and shows the distribution of the contribution of streams by length. As you can see the direct contribution meaning streams of length 1 is only about a third. While streams of length 2 contribute significantly more per average in the corpus of networks examined. Wanting to give a real life use of the contribution matrix I present the judgement of the influence of study risk of bias as is performed in cinema. The web application we developed for judging the results of network meta-analysis. In this example you can see a series of barge arts each one referring to a network effect and its block within to a single study. Blocks have size proportional to the study's contribution and color according to the study's risk of bias. As you can see although for each comparison the exact same studies are included The distribution of risk of bias differs substantially. That's the end of my presentation. Thank you very much for your attention.