Network meta-analysis (NMA) pools evidence on multiple treatments to estimate relative treatment effects. Included studies are typically assessed for risk of bias; however, this provides no indication of the impact of potential bias on a decision based on the NMA. We propose methods to derive bias adjustment thresholds which measure the smallest changes to the data that result in a change of treatment decision. The methods use efficient matrix operations and can be applied to explore the consequences of bias in individual studies or aggregate treatment contrasts, in both fixed and random-effects NMA models. Complex models with multiple types of data input are handled by using an approximation to the hypothetical aggregate likelihood. The methods are illustrated with a simple NMA of thrombolytic treatments and a more complex example comparing social anxiety interventions. An accompanying R package is provided.

中文翻译：

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建议在网络荟萃分析中偏倚的治疗敏感性。

网络荟萃分析（NMA）汇集了多种治疗的证据，以估计相对治疗效果。通常评估纳入研究的偏倚风险；但是，这不能表示潜在偏见对基于NMA的决策的影响。我们提出了导出偏差调整阈值的方法，这些阈值可测量导致治疗决策发生变化的数据的最小变化。该方法使用有效的矩阵运算，可用于在固定效应和随机效应NMA模型中探索个体研究或总体治疗对比中的偏倚后果。通过使用对假设的总似然的近似来处理具有多种类型的数据输入的复杂模型。通过简单的NMA溶栓治疗方法和比较社会焦虑干预的更复杂示例说明了这些方法。提供了随附的R包。