贝叶斯概率
荟萃分析
计算机科学
频数推理
统计
计量经济学
贝叶斯推理
随机效应模型
推论
先验概率
贝叶斯统计
机器学习
元回归
贝叶斯网络
贝叶斯因子
数学
置信区间
协变量
标识
DOI:10.1080/10543406.2020.1852247
摘要
Network meta-analysis (NMA) is a popular tool to synthesize direct and indirect evidence for simultaneously comparing multiple treatments, while evidence inconsistency greatly threatens its validity. One may use the inconsistency degrees of freedom (ICDF) to assess the potential that an NMA might suffer from inconsistency. Multi-arm studies provide intrinsically consistent evidence and complicate the ICDF's calculation; they commonly appear in NMAs. The existing ICDF measure may not feasibly handle multi-arm studies. Motivated from the effective numbers of parameters of Bayesian hierarchical models, we propose new ICDF measures in generic NMAs that may contain multi-arm studies. Under the fixed- or random-effects setting, the new ICDF measure is the difference between the effective numbers of parameters of the consistency and inconsistency NMA models. We used artificial NMAs created based on an illustrative example and 39 empirical NMAs to evaluate the performance of the existing and new measures. In NMAs with two-arm studies only, the proposed ICDF measure under the fixed-effects setting was nearly the same with the existing measure. Among the empirical NMAs, 27 (69%) contained at least one multi-arm study. The existing measure was not applicable to them, while the proposed measures led to interpretable ICDFs in all NMAs.
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