Algorithmic parameterization of mixed treatment comparisons
Algorithmic parameterization of mixed treatment comparisons
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DOI：
10.1007/s11222-011-9281-9
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发表时间：
2012-09-01
影响因子：
2.2
通讯作者：
Hillege, Hans
中科院分区：
文献类型：
--
作者：
van Valkenhoef, Gert;Tervonen, Tommi;Hillege, Hans
混合治疗比较（MTCs）可以对临床试验网络进行同步荟萃分析（数据汇集），比较千分之二的替代治疗。不一致性模型在MTC中对于评估证据源之间的总体一致性至关重要。只有在不存在相当大的不一致性的情况下，MTC（一致性）模型的结果才是可信的。然而，当证据结构中存在多组试验时，不一致的模型规格是不平凡的。在本文中，我们定义了数学术语的不一致性模型的参数化问题，并提供了一个算法的不一致性模型的生成。我们通过为15个已发表的证据结构生成模型来评估算法的运行时间。
Mixed Treatment Comparisons (MTCs) enable the simultaneous meta-analysis (data pooling) of networks of clinical trials comparing a parts per thousand yen2 alternative treatments. Inconsistency models are critical in MTC to assess the overall consistency between evidence sources. Only in the absence of considerable inconsistency can the results of an MTC (consistency) model be trusted. However, inconsistency model specification is non-trivial when multi-arm trials are present in the evidence structure. In this paper, we define the parameterization problem for inconsistency models in mathematical terms and provide an algorithm for the generation of inconsistency models. We evaluate running-time of the algorithm by generating models for 15 published evidence structures.