Power and Commensurate Priors for Synthesizing Aggregate and Individual Patient Level Data in Network Meta-Analysis
提出贝叶斯分层网络荟萃分析模型，通过幂先验和相称先验自适应地整合汇总数据与个体患者数据，并纳入协变量-治疗交互作用以支持个性化决策，通过模拟和糖尿病治疗实例验证。
Summary In network meta-analysis, it is often desirable to synthesize different types of studies, featuring aggregated data and individual patient level data. However, existing methods do not sufficiently consider the quality of studies across different types of data and assume that the treatment effects are exchangeable across all studies regardless of these types. We propose Bayesian hierarchical network meta-analysis models that allow us to borrow information adaptively across aggregated data and individual patient level data studies by using power and commensurate priors. The power parameter in the power priors and spike-and-slab hyperprior in the commensurate priors govern the level of borrowing information among study types. We incorporate covariate-by-treatment interactions to deliver personalized decision making and model any ecological fallacy. The methods are validated and compared via extensive simulation studies and then applied to an example in diabetes treatment comparing 28 oral antidiabetic drugs. We compare results across model and hyperprior specifications. Finally, we close with a discussion of our findings, limitations and future research.