Hong Hwanhee, Chu Haitao, Zhang Jing, Carlin Bradley P
Department of Mental Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55405, USA.
Res Synth Methods. 2016 Mar;7(1):6-22. doi: 10.1002/jrsm.1153. Epub 2015 Nov 4.
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two treatments), and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast-based and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., missing completely at random, missing at random, and missing not at random) providing evidence that our models outperform existing models in terms of bias, mean squared error, and coverage probability then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development.
贝叶斯统计方法用于混合治疗比较（MTCs）因其灵活性和可解释性而越来越受欢迎。许多随机临床试验报告了多个可能存在内在相关性的结果。此外，MTC数据通常很稀疏（尽管比仅比较两种治疗方法的标准荟萃分析更丰富），并且研究人员通常根据在先前试验中表现更优的治疗方法来选择研究组。在本文中，我们总结了现有的针对单一结果的MTCs分层贝叶斯方法，并同时引入了针对多个结果的新颖贝叶斯方法，而不是进行单独的MTC分析。我们通过基于对比和基于研究组的参数化方法纳入部分观测数据及其结果之间的相关结构来实现这一点，该方法将任何未观测到的治疗组视为待插补的缺失数据。我们还将模型扩展到适用于所有类型的广义线性模型结果，如计数或连续响应。我们在各种缺失机制（例如，完全随机缺失、随机缺失和非随机缺失）下进行了模拟研究，结果表明我们的模型在偏差、均方误差和覆盖概率方面优于现有模型，然后用一个真实的MTC数据集说明了我们的方法。最后，我们讨论了研究结果、MTC分析中的几个有争议的问题以及未来方法学发展的一些途径。
