Naing Cho, Reid Simon A, Aung Kyan
School of Postgraduate Studies, International Medical University, Kuala Lumpur, 5700, Malaysia.
School of Public Health, University of Queensland, Brisbane, Australia.
BMC Infect Dis. 2017 Jan 5;17(1):29. doi: 10.1186/s12879-016-2145-3.
Network meta-analysis consists of simultaneous analysis of both direct comparisons of interventions within randomized controlled trials and indirect comparisons across trials based on a common comparator. In this paper, we aimed to characterise the conceptual understanding and the rationale for the use of network meta-analysis in assessing drug efficacy.
We selected randomized controlled trials, assessing efficacy of antibiotics for the treatment of leptospirosis as a case study. A pairwise meta-analysis was conducted using a random effect model, assuming that different studies assessed different but related treatment effects. The analysis was then extended to a network meta-analysis, which consists of direct and indirect evidence in a network of antibiotics trials, using a suite of multivariate meta-analysis routines of STATA (mvmeta command). We also assessed an assumption of 'consistency' that estimates of treatment effects from direct and indirect evidence are in agreement.
Seven randomised controlled trials were identified for this analysis. These RCTs assessed the efficacy of antibiotics such as penicillin, doxycycline and cephalosporin for the treatment of human leptospirosis. These studies made comparisons between antibiotics (i.e. an antibiotic versus alternative antibiotic) in the primary study and a placebo, except for cephalosporin. These studies were sufficient to allow the creation of a network for the network meta-analysis; a closed loop in which three comparator antibiotics were connected to each other through a polygon. The comparison of penicillin versus the placebo has the largest contribution to the entire network (31.8%). The assessment of rank probabilities indicated that penicillin presented the greatest likelihood of improving efficacy among the evaluated antibiotics for treating leptospirosis.
Findings suggest that network meta-analysis, a meta-analysis comparing multiple treatments, is feasible and should be considered as better precision of effect estimates for decisions when several antibiotic options are available for the treatment of leptospirosis.
网络荟萃分析包括对随机对照试验中干预措施的直接比较以及基于共同对照在各试验间进行的间接比较同时进行分析。在本文中，我们旨在描述对网络荟萃分析在评估药物疗效方面的概念理解及其使用原理。
我们选择评估抗生素治疗钩端螺旋体病疗效的随机对照试验作为案例研究。使用随机效应模型进行成对荟萃分析，假设不同研究评估的是不同但相关的治疗效果。然后将分析扩展至网络荟萃分析，该分析使用STATA的一套多变量荟萃分析程序（mvmeta命令），涵盖抗生素试验网络中的直接和间接证据。我们还评估了“一致性”假设，即直接和间接证据得出的治疗效果估计值是一致的。
本次分析确定了7项随机对照试验。这些随机对照试验评估了青霉素、多西环素和头孢菌素等抗生素治疗人类钩端螺旋体病的疗效。除头孢菌素外，这些研究在主要研究中对不同抗生素（即一种抗生素与另一种抗生素）与安慰剂进行了比较。这些研究足以构建用于网络荟萃分析的网络；这是一个闭环，其中三种对照抗生素通过一个多边形相互连接。青霉素与安慰剂的比较对整个网络的贡献最大（31.8%）。排序概率评估表明，在评估的用于治疗钩端螺旋体病的抗生素中，青霉素提高疗效的可能性最大。
研究结果表明，网络荟萃分析这种比较多种治疗方法的荟萃分析是可行的，当有多种抗生素可用于治疗钩端螺旋体病时，应将其视为在决策时能更精确估计疗效的方法。
