The complexity underlying treatment rankings: how to use them and what to look at.
The complexity underlying treatment rankings: how to use them and what to look at.
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DOI：
10.1136/bmjebm-2021-111904
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发表时间：
2023-06
影响因子：
5.8
通讯作者：
Salanti G
中科院分区：
文献类型：
--
作者：
Chiocchia V;White IR;Salanti G
在临床领域，有几种相互竞争的治疗方法，网络荟萃分析（NMA）已成为一种既定的工具，为基于证据的决策提供信息。1 2为了确定哪种治疗方法是最可取的，决策者必须通过考虑疗效和安全性结果以及评估对所获得结果的信心来考虑可用证据的数量和质量。3然而，越来越普遍的做法是，在国家生态系统评估产出中列入对某一特定利益结果的竞争性干预措施的排序。4这篇文章的重点是这种类型的排名。通过对特定的排名度量进行排序来获得治疗（或排名）的层次结构。排名指标是衡量干预措施性能的统计数据，根据估计的相对治疗效果及其在NMA中的不确定性计算。5一个常用的排序指标是相对于自然常见对照物（如安慰剂）的相对治疗效应的点估计值。排名不受比较器选择的影响，因此可以选择任何比较器。[6]其他常用的指标是产生最佳结果值的概率，pBV（有时称为最佳概率），以及累积排名曲线下的曲面（SUCRA）或其频率论等价物，P得分。7治疗层级是一种简单直接的方式，可以显示干预措施的相对绩效并帮助决策过程，因此现在大多数出版物和报告都提供排名。4此外，正在开发新的排名指标，以获得重要的临床和方法学方面的治疗层次，如多个结果（收益和风险），临床上重要的差异和证据的质量。排名指标在文献中因缺乏可靠性而受到批评，引用，除其他问题外，有限的可解释性和“不稳定性”。8-11这种批评是基于不同的排名指标所获得的层次结构之间的分歧。例如，考虑图1中的不同治疗层次结构，这些治疗层次结构是通过9种抗高血压药物的网络的不同排名指标获得的，用于心血管疾病的一级预防12 - 13（网络图见图2）。基于pBV的治疗层级与基于相对治疗效应和SUCRA的其他层级明显不一致，特别是在最高治疗方面。传统疗法是一种定义不清的治疗方法，仅在一项试验中进行了评估，
In clinical fields where several competing treatments are available, network meta-analysis (NMA) has become an established tool to inform evidence-based decisions. 1 2 To determine which treatment is the most preferable, decision-makers must account for both the quantity and the quality of the available evidence by considering both efficacy and safety outcomes as well as assessing the confidence in the obtained results. 3 It is, however, increasingly common to include in the NMA output a ranking of the competing interventions for a specific outcome of interest. 4 This article focuses on this type of rankings. A hierarchy of treatments (or ranking) is obtained by ordering a specific ranking metric. A ranking metric is a statistic measuring the performance of an intervention and is calculated from the estimated relative treatment effects and their uncertainty in NMA. 5 A commonly used ranking metric is the point estimate of the relative treatment effects against a natural common comparator such as placebo. The rankings are unaffected by choice of comparator, so any comparator may be chosen. 6 Other commonly used metrics are the probability of producing the best outcome value, pBV (sometimes called probability of being the best), and the surface under the cumulative ranking curve (SUCRA) or their frequentist equivalent, the P-score. 7 Treatment hierarchies are a simple and straightforward way to display the relative performance of an intervention and aid the decision-making process, so nowadays most publications and reports present rankings. 4 Furthermore, new ranking metrics are being developed to obtain treatment hierarchies that account for important clinical and methodological aspects, such as multiple outcomes (benefits and risks), clinically important differences and the quality of the evidence.Ranking metrics have been criticised in the literature for their lack of reliability, quoting, among other issues, limited interpretability and ‘instability’. 8–11 This criticism was based on the disagreement between hierarchies obtained by the different ranking metrics. Consider for example the different treatment hierarchies in figure 1 obtained by different ranking metrics for a network of nine antihypertensives for primary prevention of cardiovascular disease 12 13 (network graph shown in figure 2). The treatment hierarchy based on pBV disagrees markedly with the other hierarchies, based on relative treatment effects and SUCRA, particularly with respect to the top treatment. Conventional therapy, an ill-defined treatment which was evaluated in only one trial, is in
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影响因子：
1.7
作者：
Mavridis, Dimitris;Porcher, Raphael;Ravaud, Philippe
通讯作者：
Ravaud, Philippe
影响因子：
39.2
作者：
Hutton, Brian;Salanti, Georgia;Moher, David
通讯作者：
Moher, David
影响因子：
39.2
作者：
Trinquart, Ludovic;Attiche, Nassima;Ravaud, Philippe
通讯作者：
Ravaud, Philippe
影响因子：
5
作者：
Salanti, Georgia;Nikolakopoulou, Adriani;Efthimiou, Orestis;Mavridis, Dimitris;Egger, Matthias;White, Ian R.
通讯作者：
White, Ian R.
影响因子：
7.2
作者：
Salanti, Georgia;Ades, A. E.;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.