The development of methods for the analysis of survival and event history data represents a major area of focus in the statistical sciences. Much of this work is motivated by problems in health, industry, insurance and demography regarding the occurrence of important events. Key elements in this work has been the use of partial likelihoods and counting processes which deal naturally with right censoring and left truncation.

Recent interest has been on practical problems for which the classical theory does not suffice. These include problems featuring dependent and other incomplete observation schemes, missing covariate values, measurement error, and the analysis of data from retrospective or response-selective studies. In addition, models incorporating latent variables are increasingly used and these are also not adequately dealt with using martingales. Finally, the last twenty years has seen a remarkable growth in the literature on multivariate survival times, recurrent events, multistate models and more general event history processes for complex outcomes. It has continued to grow as such models find more and more frequent application. These and other developments have lead to a rich array of problems for statistical theory and methodology. New models have been proposed and many methods of inference have been studied, including semiparametric maximum likelihood, composite and pseudo-likelihood, estimating functions, and Bayesian methods.

The purpose of this workshop is to bring together top international researchers in statistics and biostatistics engaged in survival and event history analysis to discuss recent advances and current challenges. The aim of this workshop is to discuss areas requiring new methodology and supporting theory.