 Ευχαριστώ και ευχαριστώ για αυτή την προσοδοσία. Είμαι ο Κωνσταντίνας Βογιουκας και είμαι πρόσοδοκτωρο-ρυσέρτσερ και της Αριστοτελίας της Θεσσαλονίκης στην Ελλάδα. Πρώτας, θα ευχαριστώ για την ευχαριστώ για την εύκολη να παίρνουμε το δρόμο μας. Στην αυτή την προσοδοσία, θα συμβουλίσουμε για ένα παράδειγμα μας called CCAR που προσπαθεί εύκολη να χρησιμοποιήσει τη δοκιμή της ευκολογικής πρόσοδοσίας στην εξαμμύση της εξαμμύσης. Πριν να ξεκινήσω, θα θα θέλω να αναπτύξω τα άλλα μέλλες της εξαμμύσης. Τοντορροστιακονίδης με έρθει να βοηθεί σε παράδειγμα και να συγχωρίζει την σύγχρονη, Αναμαυρωμανόλη και Αναμετίνα Χάιδης με έρθει να παίρνει ένα πρόσοδοσία με χρησιμοποιείται και εξαμμύσης. Το Βασικογεδικογένωσης στο πονοδιωστικό διωτισμό, το πονοδιωσμό στην αυλη χαρική των πιο στις ihn, εξοδο αξύγεσμα, εξοδοσιασμό και εξαμμμυσμό, απευγενείες συνταγές της φορμασίας για εκείονη εξοδοσιασμό. Τα συστηματικά εξοδοσιασμό, οι οποίοι είναι ο first level of evident synthesis και όμως your reviews, οι οποίοι είναι the second level of evident synthesis μπορεί να δημιουργήσει στις λένεις με την εξαιρετική εξαιρετική στις δημιουργίες. Λοιπόν, η δημιουργία της σύνθεσης για ένα επίπεδο της δημιουργίας είναι η επίπεδο της δημιουργίας. One of the key methodological challenges unique to overviews is the management of overlapping data due to the inclusion of the same primary studies in the reviews. The researchers may need to explore the degree of overlap at the overview level and the outcome level. The correct covered area CCA index has been used as a quantitative measure of the extent of primary study overlap across the included reviews. In order to calculate the CCA index we needed to create a matrix which is usually called citation matrix. All functions included in the CCAR packets use the CCA index. There are two versions of the formula, the original CCA index and the adjusted one. If the authors consider structural missingness when they create the citation matrix, the adjusted version is calculated automatically by the functions of the packets. Structural missingness refers to the missing data of the matrix for a logical reason. For example, primary studies were published after the contact of a specific systematic review. Therefore, it was not possible to be included in the review. This is a chronological structural missingness. As we can see in the CCA formula, the N is the sum of ones in the citation matrix, R equals to the number of rows of the matrix corresponding to the unique primary studies in the reviews. C is the number of columns of the matrix corresponding to the number of reviews and the X in the adjusted formula is the sum of the X's in the matrix which denote the structural missingness. Additionally, visual methods such as van and Euler diagrams, upset plots, hitmaps and node length graphs can be used for depicting potential overlap in overview of reviews. For the rest of the presentation, I am going to focus on the CCAAR packets which is available at github.com. It can be downloaded using the install underscore github function from the DevTools packets. First, the functions of the CCAAR packets expect the data frame which is the citation matrix and may be a first view of the overlapping reviews. In this matrix, the first row should contain the names of the included reviews and the first column should contain the names of the unique primary studies included in the reviews. The data frame must contain ones and zeros as well as missing values in case of structural missingness. One should be used to indicate if the primary study has been cited, otherwise zero. Of course, any missing value in the citation matrix affects the results. The package includes three main functions, the CCA function, the CCA underscore table function and the CCA underscore hitmap function. The citation matrix, denoted as cm, is the first argument for each function. Additionally, all functions enable the incorporation of structural missingness in the matrix. The first function, CCA, calculates the overall CCA index for the entire citation matrix as a proportion and the percentage. Larger values indicate greater overlap of primary studies across the reviews. The second function, CCA underscore table, creates a data frame with the pairwise CCA calculations from the citation matrix. The third function, CCA underscore hitmap, uses the ggplot2r packets generating an upper triangle hitmap which shows the CCA calculations as a percentage for all possible pairs of reviews. The gray diagonal tiles in the graph present information about the number of Cindy primary studies as well as the total number of primary studies included in each review. There are some advantages of the hitmap plot produced by the CCAR packets. The plot is highly customized as a ggplot object. It uses a sequential continuous color scale. It enables the incorporation of structural missingness in the citation matrix. It presents the single and total number of primary studies included in each review and it is a publication ready plot. Finally, a signing up is under development for those users who are unfamiliar with a command line environment. And here are some relative references. You can find more details about the packets in our recently published paper with the title CCAR, a packets for assessing primary study overlap across systematic reviews in overviews. Thank you very much for your attention.