Dataset for: Quantifying indirect evidence in network meta-analysis

Network meta-analysis enables comprehensive synthesis of evidence concerning multiple treatments and their simultaneous comparisons based on both direct and indirect evidence. A fundamental pre-requisite of network meta-analysis is the consistency of evidence that is obtained from different sources, particularly whether direct and indirect evidence are in accordance with each other or not, and how they may influence the overall estimates. We have developed an efficient method to quantify indirect evidence, as well as a testing procedure to evaluate their inconsistency using Lindsay's composite likelihood method. We also show that this estimator has complete information for the indirect evidence. Using this method, we can assess the degree of consistency between direct and indirect evidence and their contribution rates to the overall estimate. Sensitivity analyses can be also conducted with this method to assess the influences of potentially inconsistent treatment contrasts on the overall results. These methods can provide useful information for overall comparative results that might be biased from specific inconsistent treatment contrasts. We also provide some fundamental requirements for valid inference on these methods concerning consistency restrictions on multi-arm trials. In addition, the efficiency of the developed method is demonstrated based on simulation studies. Applications to a network meta-analysis of 12 new-generation antidepressants are presented.

网络Meta分析（Network Meta-Analysis）能够基于直接证据与间接证据，对多种治疗手段的相关证据进行综合合成，并开展多处理间的同步比较。网络Meta分析的核心前提之一，是不同来源所得证据的一致性，尤其需考察直接证据与间接证据是否相符，以及二者对总体效应估计的影响方式。本研究开发了一种可量化间接证据的高效方法，并基于林赛（Lindsay）复合似然法构建了用于评估证据不一致性的检验流程；同时证明该估计量可完整获取间接证据的信息。借助该方法，可评估直接与间接证据间的一致性程度，以及二者对总体效应估计的贡献占比。此外还可通过该方法开展敏感性分析，以考察存在潜在不一致性的处理对比对总体研究结果的影响。上述方法可为可能因特定不一致处理对比而产生偏倚的总体比较结果提供有效参考信息。本研究还针对多臂试验的一致性约束条件，明确了该类方法开展有效统计推断所需的基本前提。此外，本研究通过模拟研究验证了所提方法的有效性，并将其应用于12种新一代抗抑郁药的网络Meta分析实例中。