2025-present: Research Scientist (Chargé de recherche), Bordeaux Population Health Research Center, INSERM U1219, France.
2021-2025: Postdoctoral Researcher, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
2021: Postdoctoral Researcher, Bordeaux Population Health Research Center, INSERM U1219, France.
2017-2020: Ph.D. in Public Health & Biostatistics, University of Bordeaux, France.
2015-2017: Master of Science in Statistics, University of Southern Brittany, France.
Research Scientist in Biostatistics at Inserm (Chargé de recherche). Member of the BIOSTAT research team.
D. Rustand, J. van Niekerk, E. T. Krainski and H. Rue. Bayesian survival, longitudinal and joint models with INLA. Book in production, 2026.
D. Alvares, J. van Niekerk, E. T. Krainski, H. Rue, and D. Rustand. Bayesian survival analysis with INLA. Statistics in Medicine. 2024; 43(20): 3975-4010.
D. Rustand, J. van Niekerk, E. T. Krainski, H. Rue & C. Proust-Lima. Fast and flexible inference for joint models of multivariate longitudinal and survival data using Integrated Nested Laplace Approximations. Biostatistics, 2023, kxad019.
D. Rustand, L. Briollais, V. Rondeau. A marginal two-part joint model for a longitudinal biomarker and a terminal event with application to advanced head and neck cancers. Pharmaceutical Statistics, 2023. doi: 10.1002/pst.2338.
D. Rustand, J. van Niekerk, H. Rue, C. Tournigand, V. Rondeau, L. Briollais. Bayesian Estimation of Two-Part Joint Models for a Longitudinal Semicontinuous Biomarker and a Terminal Event with R-INLA: Interests for Cancer Clinical Trial Evaluation. Biometrical Journal, 2023, 65, 2100322.
D. Rustand, L. Briollais, C. Tournigand, V. Rondeau. Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data. Biostatistics, 2020. 10.1093/biostatistics/kxaa012.
My research focuses on the development of statistical methods for the joint analysis of multivariate longitudinal markers and time-to-event outcomes. I am particularly interested in:
Joint Modeling: Developing flexible frameworks for survival and longitudinal data analysis.
Bayesian Inference: Integrated Nested Laplace Approximations (INLA) for fast and efficient inference of complex models.
Software Development: Author of the INLAjoint R package, facilitating multivariate joint modeling with INLA.
