Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.
Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, No. 111, Section 3, Xinglong Road, Taipei, 116, Taiwan.
BMC Med Res Methodol. 2024 Oct 9;24(1):235. doi: 10.1186/s12874-024-02355-7.
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.
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.
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.
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.
网络荟萃分析旨在比较所有可用的治疗方法;因此,它丰富了临床决策的证据,为面临多种选择时提供了关于治疗效果和安全性的深入了解。然而,网络荟萃分析中的复杂性和众多治疗比较可能会给医疗保健提供者和患者带来挑战。本研究旨在引入一种图形设计,全面展示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”,以总结获得最佳治疗效果的概率(P-best)和全局指标,包括累积排序曲线下面积(SUCRA)和 P 评分。该工具通过“rankinma”R 包实现,可总结网络荟萃分析中不同结局的排名,该包已在 Comprehensive R Archive Network (CRAN) 上正式发布。它包括用于生成串珠图的PlotBead()函数,该函数表示各种结局之间的治疗排名。
串珠图基于数轴图设计,可有效地显示各种结局中每种治疗方法的综合指标。-轴上的顺序来自 P-best、SUCRA 和 P 评分等排名指标。连续线表示结局,彩色珠子表示治疗方法。
串珠图是一种直观显示多种结局治疗方法排名的有价值的图形,增强了读者的友好性,并在复杂的网络证据情况下辅助决策。虽然它可以帮助临床医生和患者确定最佳治疗方法,但应谨慎使用,并结合对整体证据确定性的评估。