论文标题
非参数贝叶斯方法在网络荟萃分析中的治疗排名与抗抑郁药的比较应用
Nonparametric Bayesian Approach to Treatment Ranking in Network Meta-Analysis with Application to Comparisons of Antidepressants
论文作者
论文摘要
网络荟萃分析是从独立研究中综合证据并同时比较多种处理的强大工具。执行网络荟萃分析的关键任务是为特定疾病结果提供所有可用治疗选择的等级。通常，估计的治疗排名伴随着大量的不确定性，遭受多重问题的困扰，并且很少允许联系。这些问题使解释排名有问题，因为它们通常被视为绝对指标。为了解决这些缺点，我们制定了一种排名策略，该策略通过产生更保守的结果来适应具有高阶不确定性的方案。这可以提高可解释性，同时考虑多次比较。为了接受治疗效果之间的联系，我们还开发了一种贝叶斯非参数方法进行网络荟萃分析。该方法利用了贝叶斯非参数方法的诱导聚类机制，产生了两种治疗效果相等的正概率。我们通过数值实验和旨在研究抗抑郁治疗的网络荟萃分析来证明该过程的实用性。
Network meta-analysis is a powerful tool to synthesize evidence from independent studies and compare multiple treatments simultaneously. A critical task of performing a network meta-analysis is to offer ranks of all available treatment options for a specific disease outcome. Frequently, the estimated treatment rankings are accompanied by a large amount of uncertainty, suffer from multiplicity issues, and rarely permit ties. These issues make interpreting rankings problematic as they are often treated as absolute metrics. To address these shortcomings, we formulate a ranking strategy that adapts to scenarios with high order uncertainty by producing more conservative results. This improves the interpretability while simultaneously accounting for multiple comparisons. To admit ties between treatment effects, we also develop a Bayesian Nonparametric approach for network meta-analysis. The approach capitalizes on the induced clustering mechanism of Bayesian Nonparametric methods producing a positive probability that two treatment effects are equal. We demonstrate the utility of the procedure through numerical experiments and a network meta-analysis designed to study antidepressant treatments.
