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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

Qiuju Li: Accommodating informative dropout and death: a joint modelling approach of longitudinal and semi-competing risks data

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