【数学与统计及交叉学科前沿论坛------高端学术讲座第118场】

报告题目：A comparison of two models for detecting inconsistency in network meta-analysis

报告人：杨柯 Assistant Professor, School of Mathematics, Statistics and Mechanics, Beijing University of Technology

报告时间：2024年9月26日（周四）14:30-15:30

报告地点：4556银河国际在线334学术讨论室

报告摘要：The application of network meta-analysis is becoming increasingly widespread, and detecting consistency assumption has always been one of the most concerned issues. The detection results can serve as a criterion for evaluating the effectiveness of network meta-analysis results. Several methods to detect inconsistency have been proposed. Among them, the design-by-treatment interaction model and the side-splitting models are most commonly used. In this paper, we compare these two types of models within a frequentist framework. By simple examples of networks with three treatments, we find that the side-splitting models are specific instances of the design-by-treatment interaction model with additional assumptions. The side-splitting models perform better when these assumptions hold. On the other hand, the design-by-treatment interaction model exhibits robust performance across different data structures. Based on our findings, we suggest to employ the design-by-treatment interaction model in practical use with the side-splitting models serving as a supplementary method for inconsistency detection in network meta-analysis.

报告人简介: Dr. Ke Yang is an Assistant Professor at School of Mathematics, Statistics and Mechanics at Beijing University of Technology. She obtained her doctoral degree in Statistics from Hong Kong Baptist University in 2021. She has published several papers in journals such as Research Synthesis Methods, Statistics and Its Interface. Her research interests include statistical methods in meta-analysis and network meta-analysis.