Institut für Biometrie, Medizinische Hochschule Hannover, Hannover, Germany.
J Clin Epidemiol. 2019 Jan;105:19-26. doi: 10.1016/j.jclinepi.2018.09.002. Epub 2018 Sep 14.
The objectives of this study were to elaborate on the conceptual evaluation of transitivity assumption in the context of binary missing participant outcome data (MOD) in network meta-analysis (NMA) and to emphasize on the importance of statistical modeling as a mean to address MOD.
We designate the notion of transitivity assumption in the context of binary MOD and indicate scenarios that compromise transitivity in complex networks. We propose a modification of these scenarios that preserves transitivity assumption. Using a published NMA, we indicate the implications of excluding or imputing, rather than modeling MOD, on NMA findings.
Arm-specific scenarios for MOD, as commonly applied in conventional meta-analysis, compromise the validity of transitivity assumption in complex networks. The motivating example reveals that imputation of those scenarios yields estimates in the opposite direction for the basic parameters with narrower credible intervals and inflates between-trial variance. Contrariwise, modeling MOD after modification of the scenarios yields robust estimates for the basic parameters but wider credible intervals and reduces between-trial variance.
Application of arm-specific scenarios for binary MOD requires modification in complex networks to ensure valid transitivity assumption. Analysts should model, rather than exclude or impute MOD, to provide bias-adjusted results.
本研究旨在详细阐述在网络荟萃分析（NMA）中二元缺失结局数据（MOD）背景下及物性假设的概念性评估，并强调统计建模作为解决 MOD 的一种方法的重要性。
我们指定了二元 MOD 背景下的及物性假设的概念，并指出了破坏复杂网络中转置性的情况。我们提出了一种修正这些情况的方法，以保持转置性假设。我们使用已发表的 NMA 来说明排除或插补而非建模 MOD 对 NMA 结果的影响。
MOD 的特定臂方案，如在常规荟萃分析中常用的那样，破坏了复杂网络中转置性假设的有效性。示例说明了，这些方案的插补会导致基本参数的估计值朝相反的方向变化，可信区间更窄，并增加了试验间方差。相反，修正方案后对 MOD 进行建模会产生稳健的基本参数估计值，但可信区间更宽，并减少了试验间方差。
在复杂网络中，应用二元 MOD 的特定臂方案需要进行修正，以确保有效的转置性假设。分析人员应进行建模，而不是排除或插补 MOD，以提供偏倚调整的结果。
