This special issue of Lifetime Data Analysis (LIDA) is comprised of papers on Bayesian methods in survival analysis. As Guest Co-Editors for the issue, we identified researchers doing interesting work in this area and invited them to submit papers. These submissions were then held to the journal’s usual high standards of peer-review. We feel that the seven papers constitute interesting advances across a broad range of topics. Two of the papers are accompanied by discussions from other researchers and a rejoinder from the authors. In the first of these, Timothy Hanson, Adam Branscum, and Wesley Johnson consider simultaneous Bayesian inference for survival times and covariate processes, i.e., joint ‘longitudinal-survival’ modeling. They use nonparametric Bayesian techniques, paying special attention to predictive criteria for model selection. Jeremy Taylor and Radu Craiu both contribute lively discussions on this article. The second discussed paper, by JosephG. Ibrahim,HongtuZhu, andNiansheng Tang, describes a comprehensive scheme to evaluate the influence of various components of a Bayesian survival model on the resulting inferences. Both Sanjib Basu and Paul Gustafson contribute discussions. Several themes cut across the papers in this issue. In addition to the aforementioned paper by Hanson et al., Huang, Li, Elashoff and Pan also consider Bayesian joint modeling of longitudinal and survival data. Cure-rate models also feature prominently. Chen, Kim, and Dey consider a model whereby a threshold structure on latent