网状Meta分析一致性的鉴别与处理方法

Dif erentiation and Handling of Homogeneity in Network Meta-analysis

与传统Meta分析相比,网状Meta分析的混杂因素较多且处理更为困难。由于证据相互传递,异质性可能会被带入间接比较中。因此,异质性的有效鉴别与正确处理备受关注。为保证网状Meta分析结果的可靠性,方法学家提出一致性概念并给出一系列鉴别与处理方法。基于Bucher法的延伸,目前一致性鉴别与处理的方法已扩展至节点分析法、假设检验及两步法等十余种量化措施。然而,由于方法学基本理论与侧重点的差异以及统计效能的局限性,使得目前相关检测与处理方法的力度仍不够高效。因此,高效、简洁、解释度高的检测与处理方法依旧需要进一步探索。

Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures （eg., node analysis method, hypothesis tests, and two-step method）. However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. 2-herefore, the exploration of highly-effective, simple and high-resolved methods are still needed,