  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
