Assessing Evidence Inconsistency in Mixed Treatment Comparisons
混合治疗比较中的证据不一致性评估AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确以随机治疗比较和治疗对比为对象，直接涉及处理效应证据的整合与一致性。
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混合治疗比较中的证据不一致性评估
- 研究问题
- 在多个随机治疗比较形成的不完全区组结构中，如何判断不同证据来源对治疗对比是否提供一致信息？
- 方法与思路
- 作者以图结构定义不一致性及其自由度，在贝叶斯层级模型中向对数优势比的线性约束加入随机不一致因子。
- 材料陈述的贡献
- 摘要称可通过含与不含不一致因子的模型比较评估整体一致性，并用因子的后验分布描述特定证据环的不一致。
- 阅读关注点
- 应核对基本参数与函数参数的区别、图结构如何确定自由度，以及模型比较和先验设定如何影响不一致性判断。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要；跨研究可比性、治疗效应的识别条件及模型比较的具体标准未完整说明。
原文摘要
Randomized comparisons among several treatments give rise to an incomplete-blocks structure known as mixed treatment comparisons (MTCs). To analyze such data structures, it is crucial to assess whether the disparate evidence sources provide consistent information about the treatment contrasts. In this article we propose a general method for assessing evidence inconsistency in the framework of Bayesian hierarchical models. We begin with the distinction between basic parameters, which have prior distributions, and functional parameters, which are defined in terms of basic parameters. Based on a graphical analysis of MTC structures, evidence inconsistency is defined as a relation between a functional parameter and at least two basic parameters, supported by at least three evidence sources. The inconsistency degrees of freedom (ICDF) is the number of such inconsistencies. We represent evidence consistency as a set of linear relations between effect parameters on the log odds ratio scale, then relax these relations to allow for inconsistency by adding to the model random inconsistency factors (ICFs). The number of ICFs is determined by the ICDF. The overall consistency between evidence sources can be assessed by comparing models with and without ICFs, whereas their posterior distribution reflects the extent of inconsistency in particular evidence cycles. The methods are elucidated using two published datasets, implemented with standard Markov chain Monte Carlo software.