About Denis Rustand Denis Rustand Postdoctoral Research Fellow (former), Statistics Survival analysis Bayesian computational statistics Denis Rustand is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Statistics), University of Southern Brittany, 2017 PhD (Biostatistics), University of Bordeaux, 2020 Research interests Bayesian computational statistics, survival analysis, applications of statistics to medical research, INLA. Awards Doctor Norbert Marx Award, 2021 Summer school grant, Univerity of Toronto, 2018 Académie Française, Jean Walter Zellidja grant, 2018 EHESP Events Presented Events Nov 19 - Nov 25, 2023 Efficient Inference for Joint Models of Multivariate Longitudinal and Survival Data Using INLAjoint Denis Rustand, Postdoctoral Research Fellow (former), Statistics Nov 23, 12:00 - 13:00 B9 L2 H2 Clinical research often requires the simultaneous study of longitudinal repeated measurements and time-to-event (i.e., survival) data. Joint models, which can combine these two types of data, are invaluable tools in this context. Oct 10 - Oct 16, 2021 Statistical methods to evaluate the effectiveness of cancer treatments in clinical trials Denis Rustand, Postdoctoral Research Fellow (former), Statistics Oct 14, 12:00 - 13:00 KAUST Assessing the effectiveness of cancer treatments in clinical trials raises multiple methodological challenges that need to be properly addressed in order to produce a reliable estimate of treatment effects.
Efficient Inference for Joint Models of Multivariate Longitudinal and Survival Data Using INLAjoint Denis Rustand, Postdoctoral Research Fellow (former), Statistics Nov 23, 12:00 - 13:00 B9 L2 H2 Clinical research often requires the simultaneous study of longitudinal repeated measurements and time-to-event (i.e., survival) data. Joint models, which can combine these two types of data, are invaluable tools in this context.
Statistical methods to evaluate the effectiveness of cancer treatments in clinical trials Denis Rustand, Postdoctoral Research Fellow (former), Statistics Oct 14, 12:00 - 13:00 KAUST Assessing the effectiveness of cancer treatments in clinical trials raises multiple methodological challenges that need to be properly addressed in order to produce a reliable estimate of treatment effects.
Engage ORCID ShareClipboard Related Sites Bayesian Computational Statistics and Modeling (BAYESCOMP) Statistics (STAT) Related Content Articles 5 Events 2
