{"title":"串珠图:对网络证据中的干预措施进行排序的新型图形。","authors":"Chiehfeng Chen, Yu-Chieh Chuang, Edwin Shih-Yen Chan, Jin-Hua Chen, Wen-Hsuan Hou, Enoch Kang","doi":"10.1186/s12874-024-02355-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Network meta-analysis is developed to compare all available treatments; therefore it enriches evidence for clinical decision-making, offering insights into treatment effectiveness and safety when faced with multiple options. However, the complexity and numerous treatment comparisons in network meta-analysis can challenge healthcare providers and patients. The purpose of this study aimed to introduce a graphic design to present complex rankings of multiple interventions comprehensively.</p><p><strong>Methods: </strong>Our team members developed a \"beading plot\" to summary probability of achieving the best treatment (P-best) and global metrics including surface under the cumulative ranking curve (SUCRA) and P-score. Implemented via the \"rankinma\" R package, this tool summarizes rankings across diverse outcomes in network meta-analyses, and the package received an official release on the Comprehensive R Archive Network (CRAN). It includes the `PlotBead()` function for generating beading plots, which represent treatment rankings among various outcomes.</p><p><strong>Results: </strong>Beading plot has been designed based on number line plot, which effectively displays collective metrics for each treatment across various outcomes. Order on the -axis is derived from ranking metrics like P-best, SUCRA, and P-score. Continuous lines represent outcomes, and color-coded beads signify treatments.</p><p><strong>Conclusion: </strong>The beading plot is a valuable graphic that intuitively displays treatment rankings across diverse outcomes, enhancing reader-friendliness and aiding decision-making in complex network evidence scenarios. While empowering clinicians and patients to identify optimal treatments, it should be used cautiously, alongside an assessment of the overall evidence certainty.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"235"},"PeriodicalIF":3.9000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462909/pdf/","citationCount":"0","resultStr":"{\"title\":\"Beading plot: a novel graphics for ranking interventions in network evidence.\",\"authors\":\"Chiehfeng Chen, Yu-Chieh Chuang, Edwin Shih-Yen Chan, Jin-Hua Chen, Wen-Hsuan Hou, Enoch Kang\",\"doi\":\"10.1186/s12874-024-02355-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Network meta-analysis is developed to compare all available treatments; therefore it enriches evidence for clinical decision-making, offering insights into treatment effectiveness and safety when faced with multiple options. However, the complexity and numerous treatment comparisons in network meta-analysis can challenge healthcare providers and patients. The purpose of this study aimed to introduce a graphic design to present complex rankings of multiple interventions comprehensively.</p><p><strong>Methods: </strong>Our team members developed a \\\"beading plot\\\" to summary probability of achieving the best treatment (P-best) and global metrics including surface under the cumulative ranking curve (SUCRA) and P-score. Implemented via the \\\"rankinma\\\" R package, this tool summarizes rankings across diverse outcomes in network meta-analyses, and the package received an official release on the Comprehensive R Archive Network (CRAN). It includes the `PlotBead()` function for generating beading plots, which represent treatment rankings among various outcomes.</p><p><strong>Results: </strong>Beading plot has been designed based on number line plot, which effectively displays collective metrics for each treatment across various outcomes. Order on the -axis is derived from ranking metrics like P-best, SUCRA, and P-score. Continuous lines represent outcomes, and color-coded beads signify treatments.</p><p><strong>Conclusion: </strong>The beading plot is a valuable graphic that intuitively displays treatment rankings across diverse outcomes, enhancing reader-friendliness and aiding decision-making in complex network evidence scenarios. While empowering clinicians and patients to identify optimal treatments, it should be used cautiously, alongside an assessment of the overall evidence certainty.</p>\",\"PeriodicalId\":9114,\"journal\":{\"name\":\"BMC Medical Research Methodology\",\"volume\":\"24 1\",\"pages\":\"235\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462909/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Research Methodology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12874-024-02355-7\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Research Methodology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12874-024-02355-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
背景:网络荟萃分析是为了比较所有可用的治疗方法而开发的;因此,它丰富了临床决策的证据,在面临多种选择时能深入了解治疗的有效性和安全性。然而,网络荟萃分析的复杂性和大量治疗比较可能会给医疗服务提供者和患者带来挑战。本研究旨在引入一种图形设计,以全面呈现多种干预措施的复杂排名:我们的团队成员开发了一种 "串珠图",用于总结获得最佳治疗的概率(P-best)和全局指标,包括累积排名曲线下表面(SUCRA)和 P 分数。该工具通过 "rankinma "R软件包实现,总结了网络荟萃分析中不同结果的排名,该软件包已在Comprehensive R Archive Network (CRAN)上正式发布。它包括用于生成串珠图的 "PlotBead() "函数,串珠图代表了各种结果之间的治疗排名:结果:串珠图是基于数字线图设计的,它有效地显示了每种治疗方法在各种结果中的集体指标。轴上的顺序来自 P-best、SUCRA 和 P-score 等排名指标。连续的线条代表结果,彩色编码的珠子代表治疗方法:串珠图是一种很有价值的图形,它能直观地显示不同结果的治疗排名,在复杂的网络证据情况下增强读者友好性并帮助决策制定。在增强临床医生和患者识别最佳治疗方法的能力的同时,应谨慎使用,并对整体证据的确定性进行评估。
Beading plot: a novel graphics for ranking interventions in network evidence.
Background: Network meta-analysis is developed to compare all available treatments; therefore it enriches evidence for clinical decision-making, offering insights into treatment effectiveness and safety when faced with multiple options. However, the complexity and numerous treatment comparisons in network meta-analysis can challenge healthcare providers and patients. The purpose of this study aimed to introduce a graphic design to present complex rankings of multiple interventions comprehensively.
Methods: Our team members developed a "beading plot" to summary probability of achieving the best treatment (P-best) and global metrics including surface under the cumulative ranking curve (SUCRA) and P-score. Implemented via the "rankinma" R package, this tool summarizes rankings across diverse outcomes in network meta-analyses, and the package received an official release on the Comprehensive R Archive Network (CRAN). It includes the `PlotBead()` function for generating beading plots, which represent treatment rankings among various outcomes.
Results: Beading plot has been designed based on number line plot, which effectively displays collective metrics for each treatment across various outcomes. Order on the -axis is derived from ranking metrics like P-best, SUCRA, and P-score. Continuous lines represent outcomes, and color-coded beads signify treatments.
Conclusion: The beading plot is a valuable graphic that intuitively displays treatment rankings across diverse outcomes, enhancing reader-friendliness and aiding decision-making in complex network evidence scenarios. While empowering clinicians and patients to identify optimal treatments, it should be used cautiously, alongside an assessment of the overall evidence certainty.
期刊介绍:
BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.