ABSTRACT

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.

摘要

网络荟萃分析（NMA）是一种流行的工具，可用于综合直接和间接证据以同时比较多种治疗方法，而证据不一致会严重威胁其有效性。人们可能会使用不一致的自由度（ICDF）来评估NMA可能遭受不一致的可能性。多臂研究提供了内在一致的证据，并使ICDF的计算复杂化；它们通常出现在NMA中。现有的ICDF措施可能无法切实可行地处理多部门研究。基于贝叶斯层次模型的有效参数数量，我们提出了可能包含多臂研究的通用NMA中的新ICDF措施。在固定或随机效果设置下，新的ICDF度量是一致性和不一致NMA模型的有效参数数量之间的差异。我们使用了基于说明性示例创建的人工NMA和39个经验NMA来评估现有和新措施的性能。在只有两臂研究的NMA中，在固定效果设置下建议的ICDF措施与现有措施几乎相同。在经验性NMA中，有27个（69％）包含至少一项多臂研究。现有措施不适用于这些措施，而拟议措施导致所有NMA都有可解释的ICDF。在固定效果设置下，拟议的ICDF措施与现有措施几乎相同。在经验性NMA中，有27个（69％）包含至少一项多臂研究。现有措施不适用于这些措施，而拟议措施导致所有NMA都有可解释的ICDF。在固定效果设置下，拟议的ICDF措施与现有措施几乎相同。在经验性NMA中，有27个（69％）包含至少一项多臂研究。现有措施不适用于这些措施，而拟议措施导致所有NMA都有可解释的ICDF。