Hawkins Neil, Scott David A, Woods Beth
ICON Health Economics, Oxford, OX2 0JJ, UK.
Centre for Health Economics, Alcuin 'A' Block, University of York, York, YO10 5DD, UK.
Res Synth Methods. 2016 Sep;7(3):306-13. doi: 10.1002/jrsm.1187. Epub 2015 Nov 27.
We present an alternative to the contrast-based parameterization used in a number of publications for network meta-analysis. This alternative "arm-based" parameterization offers a number of advantages: it allows for a "long" normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi-arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm-based parameterization allows simple extension to treatment-specific random treatment effect variances. We validated the parameterization using a published smoking cessation dataset. Network meta-analysis using arm- and contrast-based parameterizations produced comparable results (with means and standard deviations being within +/- 0.01) for both fixed and random effects models. We recommend that analysts consider using arm-based parameterization when carrying out network meta-analyses. © 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
我们提出了一种替代方法，用于替代许多网络荟萃分析出版物中使用的基于对比的参数化方法。这种替代性的“基于组”参数化方法具有许多优点：它允许构建一个“长”的标准化数据结构，无论比较器的数量如何，该结构都保持不变；它可用于直接将个体患者数据纳入分析；纳入多组试验很简单，并且无需生成描述治疗效果的多元分布；在诸如WinBUGS等语言中，参数化与分析脚本之间存在直接映射，最后，基于组的参数化允许简单扩展到特定治疗的随机治疗效果方差。我们使用已发表的戒烟数据集验证了该参数化方法。对于固定效应模型和随机效应模型，使用基于组和基于对比的参数化方法进行网络荟萃分析产生了可比的结果（均值和标准差在±0.01范围内）。我们建议分析人员在进行网络荟萃分析时考虑使用基于组的参数化方法。 © 2015作者 研究综合方法 由John Wiley & Sons Ltd出版
