Systematic reviews and meta-analyses synthesize results from well-conducted studies to optimize healthcare decision-making. Network meta-analysis (NMA) is particularly useful for improving precision, drawing new comparisons, and ranking multiple interventions. However, recommendations can be misled if published results are a selective sample of what has been collected by trialists, particularly when publication status is related to the significance of the findings. Unfortunately, the missing-not-at-random nature of this problem and the numerous parameters involved in modeling NMAs pose unique computational challenges to quantifying and correcting for publication bias, such that sensitivity analysis is used in practice. Motivated by this important methodological gap, we developed a novel and stable expectation-maximization (EM) algorithm to correct for publication bias in the network setting. We validate the method through simulation studies and show that it achieves substantial bias reduction in small to moderately sized NMAs. We also calibrate the method against a Bayesian analysis of a published NMA on antiplatlet therapies for maintaining vascular patency.

中文翻译：

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EMBRACE：一种基于 EM 的偏差减少方法，通过 Copas 模型估计量化网络荟萃分析中选择性发布的证据

系统评价和荟萃分析综合了进行良好研究的结果，以优化医疗保健决策。网络荟萃分析 (NMA) 对于提高精确度、进行新的比较和对多种干预措施进行排名特别有用。然而，如果发表的结果是试验者收集的选择性样本，特别是当发表状态与研究结果的重要性相关时，建议可能会被误导。不幸的是，这个问题的缺失非随机性质和 NMA 建模中涉及的众多参数对量化和纠正发表偏差提出了独特的计算挑战，因此在实践中使用敏感性分析。在这一重要的方法论差距的推动下，我们开发了一种新颖且稳定的期望最大化（EM）算法来纠正网络环境中的发表偏差。我们通过模拟研究验证了该方法，并表明它在中小型 NMA 中实现了显着的偏差减少。我们还根据已发表的关于维持血管通畅的抗血小板疗法的 NMA 的贝叶斯分析来校准该方法。