Phillippo David M, Dias Sofia, Ades A E, Didelez Vanessa, Welton Nicky J
University of Bristol UK.
Leibniz Institute for Prevention Research and Epidemiology, and University of Bremen Germany.
J R Stat Soc Ser A Stat Soc. 2018 Jun;181(3):843-867. doi: 10.1111/rssa.12341. Epub 2017 Dec 6.
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.
网络荟萃分析（NMA）汇总多种治疗方法的证据，以估计相对治疗效果。通常会对纳入的研究进行偏倚风险评估；然而，这并未表明潜在偏倚对基于NMA做出的决策的影响。我们提出了推导偏倚调整阈值的方法，这些阈值可衡量数据中导致治疗决策改变的最小变化。这些方法使用高效的矩阵运算，可应用于探究固定效应和随机效应NMA模型中个体研究或总体治疗对比中偏倚的后果。通过对假设的总体似然性进行近似处理，可处理具有多种类型数据输入的复杂模型。通过溶栓治疗的简单NMA和比较社交焦虑干预措施的更复杂示例对这些方法进行了说明。并提供了一个配套的R包。
