Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre-University of Freiburg, Freiburg, Germany.
Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
BMC Med Res Methodol. 2022 Feb 17;22(1):47. doi: 10.1186/s12874-021-01488-3.
Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question.
We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics.
We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%.
The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments.
网络荟萃分析估计了竞争治疗方法之间的所有相对效果，并可根据健康结果从最理想到最不理想的治疗方案排列出一个治疗等级。尽管大约一半的已发表网络荟萃分析呈现出这样的等级，但很少有等级与临床相关决策问题相关。
我们首先定义了网络荟萃分析中的治疗等级和治疗排序，并提出了一种模拟方法来估计每种可能等级出现的概率。然后，我们提出了一种逐步的方法，将临床相关决策问题表示为等级问题，并量化构成它们的标准的不确定性。该方法的步骤总结如下：a）定义临床相关问题，b）收集满足定义问题的等级，c）添加各自等级的频率；得到的总和表示定义的标准集成立的确定性。然后，我们展示了所有可能等级的频率与常见排序指标的关系。
我们使用两个网络来举例说明该方法及其实现。第一个是慢性阻塞性肺疾病的四种治疗方法的网络，最可能的等级频率为 28%。第二个是 18 种抗抑郁药的网络，其中文拉法辛、安非他酮和依他普仑以频率 19%占据前三个等级。
所开发的方法提供了一种在网络荟萃分析中生成治疗等级的通用方法，它将治疗排序与所有或部分竞争治疗相关的明确决策问题联系起来。
