Division of Clinical Pharmacology and Toxicology, Geneva University Hospital, Geneva, Switzerland.
Research Center for Statistics, Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland.
Res Synth Methods. 2017 Sep;8(3):263-274. doi: 10.1002/jrsm.1236. Epub 2017 Apr 21.
Meta-analysis can necessitate the combination of parallel and cross-over trial designs. Because of the differences in the trial designs and potential biases notably associated with the crossover trials, one often combines trials of the same designs only, which decreases the power of the meta-analysis. To combine results of clinical trials from parallel and cross-over designs, we extend the method proposed in an accompanying study to account for random effects. We propose here a hierarchical mixed model allowing the combination of the 2 types of trial designs and accounting for additional covariates where random effects can be introduced to account for heterogeneity in trial, treatment effect, and interactions. We introduce a multilevel model and a Bayesian hierarchical model for combined trial design meta-analysis. The analysis of the models by restricted iterative generalised least square and Monte Carlo Markov Chain is presented. Methods are compared in a combined design meta-analysis model on salt reduction. Both models and their respective advantages in the perspective of meta-analysis are discussed. However, the access to the trial data, in particular sequence and period data in cross-over trials, remains a major limitation to the meta-analytic combination of trial designs.
荟萃分析可能需要结合平行试验设计和交叉试验设计。由于试验设计的差异以及交叉试验中明显存在的潜在偏倚，人们通常只结合相同设计的试验，这降低了荟萃分析的效力。为了合并来自平行和交叉设计的临床试验结果，我们扩展了伴随研究中提出的方法，以考虑随机效应。我们在这里提出了一个层次混合模型，允许合并这两种试验设计，并考虑其他协变量，其中随机效应可以引入，以解释试验、治疗效果和交互作用中的异质性。我们引入了一个多水平模型和一个贝叶斯层次模型，用于联合试验设计荟萃分析。通过限制迭代广义最小二乘法和蒙特卡罗马尔可夫链对模型进行了分析。在盐减少的联合设计荟萃分析模型中比较了这些方法。讨论了两种模型及其在荟萃分析中的各自优势。然而，获取试验数据，特别是交叉试验中的序列和周期数据，仍然是荟萃分析中合并试验设计的一个主要限制。
