Dataset for: A Bayesian approach to sequential meta-analysis

As evidence accumulates within a meta-analysis, it is desirable to determine when the results could be considered conclusive to guide systematic review updates and future trial designs. Adapting sequential testing methodology from clinical trials for application to pooled meta-analytic effect size estimates appears well suited for this objective. In this paper we describe a Bayesian sequential meta-analysis method, in which an informative heterogeneity prior is employed and stopping rule criteria are applied directly to the posterior distribution for the treatment effect parameter. Using simulation studies, we examine how well this approach performs under different parameter combinations by monitoring the proportion of sequential meta-analyses that reach incorrect conclusions (to yield error rates), the number of studies required to reach conclusion, and the resulting parameter estimates. By adjusting the stopping rule thresholds, the overall error rates can be controlled within the target levels and are no higher than those of alternative frequentist and semi-Bayes methods for the majority of the simulation scenarios. To illustrate the potential application of this method, we consider two contrasting meta-analyses using data from the Cochrane Library and compare the results of employing different sequential methods, while examining the effect of the heterogeneity prior in the proposed Bayesian approach.

随着荟萃分析（meta-analysis）中证据的持续积累，亟需明确研究结果何时可被认定为结论性，以指导系统综述的更新与未来试验的设计。将临床试验中的序贯检验方法适配应用于合并后的荟萃分析效应量估计，似乎十分契合这一研究目标。本文提出一种贝叶斯序贯荟萃分析方法，该方法采用信息异质性先验，并将终止准则直接应用于治疗效应参数的后验分布。通过模拟研究，我们通过监测得出错误结论的序贯荟萃分析占比（即错误率）、达成结论所需的研究数量，以及最终得到的参数估计结果，考察该方法在不同参数组合下的表现性能。通过调整终止准则阈值，整体错误率可被控制在目标水平内，且在大多数模拟情景中，该错误率不高于其他频率学派与半贝叶斯方法的错误率。为阐明该方法的潜在应用价值，我们采用考克兰图书馆（Cochrane Library）中的数据开展两项对比性荟萃分析，对比不同序贯方法的分析结果，同时考察所提出的贝叶斯方法中异质性先验的影响效果。