1 Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
2 Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Stat Methods Med Res. 2019 May;28(5):1293-1310. doi: 10.1177/0962280218754928. Epub 2018 Feb 13.
Meta-analysis of interventions usually relies on randomized controlled trials. However, when the dominant source of information comes from single-arm studies, or when the results from randomized controlled trials lack generalization due to strict inclusion and exclusion criteria, it is vital to synthesize both sources of evidence. One challenge of synthesizing both sources is that single-arm studies are usually less reliable than randomized controlled trials due to selection bias and confounding factors. In this paper, we propose a Bayesian hierarchical framework for the purpose of bias reduction and efficiency gain. Under this framework, three methods are proposed: bivariate generalized linear mixed effects models, hierarchical power prior model and hierarchical commensurate prior model. Design difference and potential biases are considered in all models, within which the hierarchical power prior and hierarchical commensurate prior models further offer to downweight single-arm studies flexibly. The hierarchical commensurate prior model is recommended as the primary method for evidence synthesis because of its accuracy and robustness. We illustrate our methods by applying all models to two motivating datasets and evaluate their performance through simulation studies. We finish with a discussion of the advantages and limitations of our methods, as well as directions for future research in this area.
元分析通常依赖于随机对照试验。然而，当主要信息来源来自单臂研究时，或者当由于严格的纳入和排除标准，随机对照试验的结果缺乏普遍性时，综合这两种来源的证据就显得至关重要。综合这两种来源的一个挑战是，由于选择偏差和混杂因素，单臂研究通常不如随机对照试验可靠。在本文中，我们提出了一个贝叶斯分层框架，旨在减少偏差和提高效率。在这个框架下，提出了三种方法：双变量广义线性混合效应模型、分层幂先验模型和分层相称先验模型。所有模型都考虑了设计差异和潜在偏差，其中分层幂先验和分层相称先验模型进一步提供了灵活地下调单臂研究的权重的方法。由于其准确性和稳健性，我们推荐使用分层相称先验模型作为主要的证据综合方法。我们通过将所有模型应用于两个激励性数据集来说明我们的方法，并通过模拟研究评估它们的性能。最后，我们讨论了我们方法的优缺点，以及该领域未来研究的方向。
