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Kossmeier M, Tran US, Voracek M. Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis. BMC Med Res Methodol 2020; 20:26. [PMID: 32028897 PMCID: PMC7006175 DOI: 10.1186/s12874-020-0911-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 01/23/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. METHODS We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. RESULTS We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features. CONCLUSIONS The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.
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Affiliation(s)
- Michael Kossmeier
- Department of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010 Vienna, Austria
| | - Ulrich S. Tran
- Department of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010 Vienna, Austria
| | - Martin Voracek
- Department of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010 Vienna, Austria
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Pan Z, Chen M, Hu X, Wang H, Yang J, Zhang C, Pan F, Sun G. Associations between VDR gene polymorphisms and colorectal cancer susceptibility: an updated meta-analysis based on 39 case-control studies. Oncotarget 2018; 9:13068-13076. [PMID: 29560132 PMCID: PMC5849196 DOI: 10.18632/oncotarget.23964] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 11/14/2017] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Recent studies have reported the associations between vitamin D receptor (VDR) polymorphisms and colorectal cancer (CRC), but the results were not always consistent. This meta-analysis aims to evaluate whether VDR polymorphisms are associated with CRC susceptibility. MATERIALS AND METHODS Studies on the associations between VDR polymorphisms and CRC were retrieved from the Web of Science, PubMed, the Chinese Biomedical Database (CBM), Chinese National Knowledge Infrastructure (CNKI) and Wanfang (Chinese) databases. The odds ratio (OR) with 95% confidence intervals (CIs) was obtained. RESULTS Thirty-nine articles met all inclusion criteria and were included in the meta-analysis including 22101 CRC cases and 23696 healthy controls. The 39 articles consisted of five VDR gene polymorphisms including ApaI, FokI, BsmI, TaqI and Cdx2. The results of meta-analysis showed that the FokI polymorphism was on the fringe of statistically significant in the comparisons of F allele vs. f allele in fixed model (OR = 1.029, 95% CI = 0.999-1.059, Praw = 0.057, PFDR = 0.057). Moreover, for the associations between BsmI polymorphism with CRC, We observed significant differences in allele frequencies, the homozygous model and the dominant model between CRC patients and healthy controls (B vs. b: OR = 0.862, 95% CI = 0.761-0.976, Praw = 0.019, PFDR = 0.019; BB vs. bb: OR = 0.786, 95% CI = 0.636-0.972, Praw = 0.026, PFDR = 0.039; BB + Bb vs. bb: OR = 0.934, 95% CI = 0.888-0.982, Praw = 0.008, PFDR = 0.024, respectively). CONCLUSIONS This meta-analysis suggests that BsmI is associated with CRC risk and FokI might be a risk factor for CRC. However, these associations with CRC need further studied.
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Affiliation(s)
- Zhipeng Pan
- Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Mengya Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, Hefei, Anhui, 230032, China
| | - Xingxing Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, Hefei, Anhui, 230032, China
| | - Hua Wang
- Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Jiajia Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, Hefei, Anhui, 230032, China
| | - Congjun Zhang
- Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Faming Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, Hefei, Anhui, 230032, China
| | - Guoping Sun
- Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
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