Konstantinos I Bougioukas1, Elpida Vounzoulaki2, Chrysanthi D Mantsiou3, Eliophotos D Savvides4, Christina Karakosta1, Theodoros Diakonidis1, Apostolos Tsapas5, Anna-Bettina Haidich6. 1. Department of Hygiene, Social-Preventive Medicine & Medical Statistics, Medical School, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece. 2. Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK; National Institute for Health Research Applied Research Collaboration-East Midlands, Leicester Diabetes Centre, Leicester LE5 4PW, UK. 3. Clinical Research and Evidence-Based Medicine Unit, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece. 4. 1st Department of Urology, Medical School, G. Gennimatas General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece. 5. Clinical Research and Evidence-Based Medicine Unit, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece; Diabetes Centre, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece; Harris Manchester College, University of Oxford, Oxford, United Kingdom. 6. Department of Hygiene, Social-Preventive Medicine & Medical Statistics, Medical School, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece. Electronic address: haidich@auth.gr.
Abstract
OBJECTIVE: To introduce potential static tabular and graphical techniques for visually presenting overlap between systematic reviews (SRs) included in overviews of systematic reviews (OoSRs). STUDY DESIGN AND SETTING: The graphical approaches described include Venn and Euler diagrams, as well as matrix-based, node-link, and aggregation-based techniques. We used fundamental concepts of mathematics from set and network theory to develop our novel graphical approaches. The graphical displays were created using R. RESULTS: Overview authors have the flexibility to choose from a variety of visualizations, depending on the characteristics of their study. If the OoSRs includes few SRs, a Venn or an Euler diagram can be used. In case of an OoSRs with more SRs, Upset plots, heatmaps and node-link graphs are more appropriate for visualizing overlapping SRs. Stacked bar plots constitute an aggregation-based technique of illustrating overlap. Strengths and limitations of each graphical approach are presented. CONCLUSION: The degree of overlap should be explored for the entire study and for specific outcomes of interest. The proposed graphical techniques may assist methodologists and authors in identifying overlap, which in turn may improve validity and transparency in OoSRs. More research is needed to understand which technique would be most useful and easiest to understand.
OBJECTIVE: To introduce potential static tabular and graphical techniques for visually presenting overlap between systematic reviews (SRs) included in overviews of systematic reviews (OoSRs). STUDY DESIGN AND SETTING: The graphical approaches described include Venn and Euler diagrams, as well as matrix-based, node-link, and aggregation-based techniques. We used fundamental concepts of mathematics from set and network theory to develop our novel graphical approaches. The graphical displays were created using R. RESULTS: Overview authors have the flexibility to choose from a variety of visualizations, depending on the characteristics of their study. If the OoSRs includes few SRs, a Venn or an Euler diagram can be used. In case of an OoSRs with more SRs, Upset plots, heatmaps and node-link graphs are more appropriate for visualizing overlapping SRs. Stacked bar plots constitute an aggregation-based technique of illustrating overlap. Strengths and limitations of each graphical approach are presented. CONCLUSION: The degree of overlap should be explored for the entire study and for specific outcomes of interest. The proposed graphical techniques may assist methodologists and authors in identifying overlap, which in turn may improve validity and transparency in OoSRs. More research is needed to understand which technique would be most useful and easiest to understand.
Authors: Hongshuo Shi; Ting Liu; Chengda Dong; Kun Zhen; Yuxuan Wang; Pengjun Liu; Guomin Si; Lei Wang; Min Wang Journal: Biomed Res Int Date: 2022-08-02 Impact factor: 3.246
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