Extensions of the probabilistic ranking metrics of competing treatments in network meta-analysis to reflect clinically important relative differences on many outcomes
Extensions of the probabilistic ranking metrics of competing treatments in network meta-analysis to reflect clinically important relative differences on many outcomes
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DOI：
10.1002/bimj.201900026
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发表时间：
2019-10-29
影响因子：
1.7
通讯作者：
Ravaud, Philippe
中科院分区：
文献类型：
--
作者：
Mavridis, Dimitris;Porcher, Raphael;Ravaud, Philippe
网络荟萃分析的关键特征之一是根据感兴趣的结果对干预措施进行排名。由于当前排名方法的两个局限性，排名指标容易被误解。首先，相对治疗效果的差异可能不具有临床重要性，并且这未反映在排名指标中。第二，没有既定的方法将几种健康结果纳入排名评估。为了解决这两个问题，我们扩展了P评分方法以允许多个结果，并对其进行了修改，以衡量一种治疗比竞争治疗好一定程度的平均确定性程度，例如，最小临床重要差异。我们建议提出有利和有害结果之间的权衡，允许利益相关者考虑他们愿意容忍多少不利影响，以获得特定的疗效。我们使用了一个已发表的212项试验网络，使用随机效应网络荟萃分析模型比较了15种抗精神病药物和安慰剂，重点关注三个结果：标准化量表中精神分裂症症状的减轻，全因停药和体重增加。
One of the key features of network meta-analysis is ranking of interventions according to outcomes of interest. Ranking metrics are prone to misinterpretation because of two limitations associated with the current ranking methods. First, differences in relative treatment effects might not be clinically important and this is not reflected in the ranking metrics. Second, there are no established methods to include several health outcomes in the ranking assessments. To address these two issues, we extended the P-score method to allow for multiple outcomes and modified it to measure the mean extent of certainty that a treatment is better than the competing treatments by a certain amount, for example, the minimum clinical important difference. We suggest to present the tradeoff between beneficial and harmful outcomes allowing stakeholders to consider how much adverse effect they are willing to tolerate for specific gains in efficacy. We used a published network of 212 trials comparing 15 antipsychotics and placebo using a random effects network meta-analysis model, focusing on three outcomes; reduction in symptoms of schizophrenia in a standardized scale, all-cause discontinuation, and weight gain.