Graphs of study contributions and covariate distributions for network meta-regression.
Graphs of study contributions and covariate distributions for network meta-regression.
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DOI：
10.1002/jrsm.1292
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发表时间：
2018-06
影响因子：
9.8
通讯作者：
Welton NJ
中科院分区：
文献类型：
--
作者：
Donegan S;Dias S;Tudur-Smith C;Marinho V;Welton NJ
在解释Meta回归结果时必须考虑到参与研究的协变量值的范围。基于内插或外推的结果可能是不可靠的。在网络荟萃分析中包含协变量的网络荟萃回归模型中，结果是使用直接和间接证据来估计的；因此，可能不清楚哪些研究和协变量的值对哪些结果有贡献。我们提出图表来帮助理解哪些试验和协变量值对每个核磁共振结果有贡献，并突出外推或内插。我们介绍了计算每个试验和协变量值对每个结果的贡献的方法，并将它们与现有方法进行比较。我们展示了如何构造图，包括网络协变量分布图、协变量-贡献图、热图、贡献-核磁共振图和热-核磁共振图。我们使用协变量平均年龄的疟疾治疗数据集和使用协变量随机年的局部氟化物干预预防龋齿的数据集来演示这些方法。对于疟疾数据集，没有任何有贡献的试验的平均年龄在7-25岁之间，因此结果是在这个范围内插入的。对于氟化物数据集，在1954-1959年间没有对大多数比较有贡献的随机试验，因此，在这个范围内，结果将被推断。即使在完全连接的网络中，核磁共振结果也可以从协变量范围比整个数据集的范围更窄的试验中估计出来。通过突出显示外推或内插结果，计算贡献并以图形显示有助于解释核磁共振结果。
Meta‐regression results must be interpreted taking into account the range of covariate values of the contributing studies. Results based on interpolation or extrapolation may be unreliable. In network meta‐regression (NMR) models, which include covariates in network meta‐analyses, results are estimated using direct and indirect evidence; therefore, it may be unclear which studies and covariate values contribute to which result. We propose graphs to help understand which trials and covariate values contribute to each NMR result and to highlight extrapolation or interpolation. We introduce methods to calculate the contribution that each trial and covariate value makes to each result and compare them with existing methods. We show how to construct graphs including a network covariate distribution diagram, covariate‐contribution plot, heat plot, contribution‐NMR plot, and heat‐NMR plot. We demonstrate the methods using a dataset with treatments for malaria using the covariate average age and a dataset of topical fluoride interventions for preventing dental caries using the covariate randomisation year. For the malaria dataset, no contributing trials had an average age between 7–25 years and therefore results were interpolated within this range. For the fluoride dataset, there are no contributing trials randomised between 1954–1959 for most comparisons therefore, within this range, results would be extrapolated. Even in a fully connected network, an NMR result may be estimated from trials with a narrower covariate range than the range of the whole dataset. Calculating contributions and graphically displaying them aids interpretation of NMR result by highlighting extrapolated or interpolated results.
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影响因子：
3.7
作者：
Donegan S;Williamson P;Gamble C;Tudur-Smith C
通讯作者：
Tudur-Smith C
影响因子：
3.7
作者：
Donegan S;Williams L;Dias S;Tudur-Smith C;Welton N
通讯作者：
Welton N
影响因子：
9.8
作者：
Lu, Guobing;Welton, Nicky J.;Ades, A. E.
通讯作者：
Ades, A. E.
影响因子：
2
作者：
Cooper, Nicola J.;Sutton, Alex J.;Welton, Nicky J.
通讯作者：
Welton, Nicky J.
影响因子：
2
作者：
Donegan, Sarah;Williamson, Paula;Smith, Catrin Tudur
通讯作者：
Smith, Catrin Tudur