Mavridis Dimitris, Welton Nicky J, Sutton Alex, Salanti Georgia
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Primary Education, University of Ioannina, Ioannina, Greece.
Stat Med. 2014 Dec 30;33(30):5399-412. doi: 10.1002/sim.6321. Epub 2014 Oct 15.
Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency.
科帕斯和施提出了一种选择模型，通过基于试验结果精度条件下的发表概率假设进行敏感性分析，来探索发表偏倚的潜在影响。乔特拉库尔等人将该模型扩展到了三臂试验，但没有充分考虑一致性假设的影响，并且他们的模型难以推广到具有三种以上治疗方法的复杂网络结构。在频率主义框架内拟合这些选择模型需要最大化一个复杂的似然函数，识别问题很常见。我们之前在将多种治疗方法与一种共同对照治疗方法进行比较时，提出了选择模型的贝叶斯实现方式。我们现在提出一种适用于复杂的全网络荟萃分析的通用模型，该模型在调整发表偏倚结果时考虑了一致性。我们开发了一种按治疗设计的选择模型，以描述具有不同设计（试验中比较的治疗组）和精度的研究可能被选择发表的机制。我们在贝叶斯框架内拟合该模型，因为它避免了频率主义框架中遇到的数值问题，在治疗方法和研究臂的数量方面具有可推广性，并且为使用外部知识进行敏感性分析提供了一个灵活的框架。与标准的科帕斯模型或其先前的扩展相比，我们的模型更成功地考虑了发表偏倚带来的额外不确定性。我们使用已发表的关于血管移植物失败或动脉通畅情况的三角网络来说明该方法。
