Department of Biostatistics, University of North Carolina at Chapel Hill, USA.
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA.
Stat Methods Med Res. 2019 Oct-Nov;28(10-11):3415-3436. doi: 10.1177/0962280218801147. Epub 2018 Oct 12.
We examine a class of multivariate meta-regression models in the presence of individual patient data. The methodology is well motivated from several studies of cholesterol-lowering drugs where the goal is to jointly analyze the multivariate outcomes, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides. These three continuous outcome measures are correlated and shed much light on a subject's lipid status. One of the main goals in lipid research is the joint analysis of these three outcome measures in a meta-regression setting. Since these outcome measures are not typically multivariate normal, one must consider classes of distributions that allow for skewness in one or more of the outcomes. In this paper, we consider a new general class of multivariate skew distributions for multivariate meta-regression and examine their theoretical properties. Using these distributions, we construct a Bayesian model for the meta-data and develop an efficient Markov chain Monte Carlo computational scheme for carrying out the computations. In addition, we develop a multivariate measure for model comparison, Bayesian residuals for model assessment, and a Bayesian procedure for detecting outlying trials. The proposed multivariate measure, Bayesian residuals, and Bayesian outlying trial detection procedure are particularly suitable and computationally attractive in the multivariate meta-regression setting. A detailed case study demonstrating the usefulness of the proposed methodology is carried out in an individual patient data multivariate meta-regression setting using 26 pivotal Merck clinical trials that compare statins (cholesterol-lowering drugs) in combination with ezetimibe and statins alone on treatment-naïve patients and those continuing on statins at baseline.
我们研究了存在个体患者数据时的一类多元荟萃回归模型。这种方法学在降脂药物的多项研究中具有很好的应用动机，这些研究的目的是联合分析多变量结局、低密度脂蛋白胆固醇、高密度脂蛋白胆固醇和甘油三酯。这三个连续的结局指标是相关的，对患者的血脂状况有很大的了解。在脂质研究的主要目标之一是在荟萃回归环境中联合分析这三个结局指标。由于这些结局指标通常不是多元正态的，因此必须考虑允许一个或多个结局出现偏态的分布类别。在本文中，我们考虑了一种新的用于多元荟萃回归的多元偏态分布的一般类，并研究了它们的理论性质。使用这些分布，我们为元数据构建了一个贝叶斯模型，并开发了一种有效的马尔可夫链蒙特卡罗计算方案来进行计算。此外，我们还开发了一种用于模型比较的多元度量、用于模型评估的贝叶斯残差和用于检测异常试验的贝叶斯程序。所提出的多元度量、贝叶斯残差和贝叶斯异常试验检测程序特别适合且在计算上具有吸引力，适用于多元荟萃回归设置。在个体患者数据多元荟萃回归设置中进行了一项详细的案例研究，使用了 26 项关键的默克临床研究，这些研究比较了他汀类药物（降脂药物）与依折麦布联合使用和他汀类药物单独用于初治患者和基线时继续使用他汀类药物的患者的疗效。
