We propose a novel nonparametric Bayesian approach for meta-analysis with event time outcomes. The model is an extension of linear dependent tail-free processes. The extension includes a modification to facilitate (conditionally) conjugate posterior updating and a hierarchical extension with a random partition of studies. The partition is formalized as a Dirichlet process mixture. The model development is motivated by a meta-analysis of cancer immunotherapy studies. The aim is to validate the use of relevant biomarkers in the design of immunotherapy studies. The hypothesis is about immunotherapy in general, rather than about a specific tumor type, therapy and marker. This broad hypothesis leads to a very diverse set of studies being included in the analysis and gives rise to substantial heterogeneity across studies

翻译：我们提出了一种新颖的非参数贝叶斯方法，用于处理事件时间结果的元分析。该模型是线性依赖无尾过程的扩展。扩展内容包括：为促进（条件）共轭后验更新而进行的改进，以及通过研究随机划分实现的层次化扩展。该划分形式化为狄利克雷过程混合模型。模型构建的动机源于一项癌症免疫治疗研究的元分析，旨在验证相关生物标志物在免疫治疗方案设计中的应用价值。研究假设针对广义的免疫治疗，而非特定肿瘤类型、疗法或标志物。这种宽泛的假设导致分析纳入的研究集具有高度多样性，从而引发研究间显著的异质性。