Analysis Group Inc., Boston, MA 02199, USA.
US HEOR, Bristol-Myers Squibb Company, Princeton, NJ 08648, USA.
J Comp Eff Res. 2020 Jul;9(10):737-750. doi: 10.2217/cer-2020-0042. Epub 2020 Jun 3.
To illustrate that bias associated with indirect treatment comparison and network meta-analyses can be reduced by adjusting for outcomes on common reference arms. Approaches to adjusting for reference-arm effects are presented within a causal inference framework. Bayesian and Frequentist approaches are applied to three real data examples. Reference-arm adjustment can significantly impact estimated treatment differences, improve model fit and align indirectly estimated treatment effects with those observed in randomized trials. Reference-arm adjustment can possibly reverse the direction of estimated treatment effects. Accumulating theoretical and empirical evidence underscores the importance of adjusting for reference-arm outcomes in indirect treatment comparison and network meta-analyses to make full use of data and reduce the risk of bias in estimated treatments effects.
为了说明通过调整共同参照臂的结局，可以减少间接治疗比较和网络荟萃分析中的偏倚。在因果推理框架内提出了调整参照臂效应的方法。贝叶斯和频率派方法应用于三个真实数据实例。参照臂调整可显著影响估计的治疗差异，改善模型拟合，并使间接估计的治疗效果与随机试验中观察到的效果一致。参照臂调整可能会改变估计的治疗效果的方向。理论和经验证据的积累强调了在间接治疗比较和网络荟萃分析中调整参照臂结局的重要性，以便充分利用数据并降低估计治疗效果的偏倚风险。
