Slides
Keynote speakers:
Michael J. Crowther: Flexible parametric joint modelling of longitudinal and survival data
Vern Farewell: Two Tools for the Analysis of Longitudinal Data: Motivations, Applications and Issues
Arnošt Komárek: Regression modelling of misclassified correlated interval-censored data
Peter Müller: Nonparametric Bayesian survival regression with variable dimension covariate vector
Cecile Proust-Lima: Examples of joint models for multivariate longitudinal and multistate processes in chronic diseases
Bernard Rachet: Missing data and net survival analysis
Dimitris Rizopoulos: Personalized Screening Intervals for Biomarkers using Joint Models for Longitudinal and Survival Data
Contributed talks:
Paul Blanche: Dynamic predictions from joint models of longitudinal and time-to-event data: a note on $R^2$-type curves
Ziqi Chen: A profile likelihood approach to longitudinal data
Walter Dempsey: Survival models and health sequences
Markus C. Elze: Incorporating reference ranges from healthy individuals in joint longitudinal and time-to-event modelling
David Hughes: Flexible Discriminant Analysis Using Multivariate Mixed Models
Jose S. Romeo: The Power Variance Function Copula Model in Bivariate Survival Analysis: An application to Twin Data
Francisco Javier Rubio: Linear mixed models with improper priors and flexible distributional assumptions for longitudinal and survival data
Catalina A. Vallejos: Incorporating unobserved heterogeneity in Weibull survival models: A Bayesian approach