Individualized Predictions, Time-varying Effects and Time-varying Covariates

Extensions of joint models for improving subject-specific predictions.

Multivariate Joint Models

Computational methods for multivariate joint models.

Personalized Active Surveilance and Screening

Novel methods for optimally planning when to collect longitudinal measurements or event information.


The current composition of my research group:

  • Sara Baart: Sara is a post-Doc student working on extending joint models in studies with special types of designs, and in particular in case-cohort designs.

  • Anja Ruten-Budde: Anja is a post-Doc student working on combinations of joint models with machine learning techniques.

  • Nicole Erler: Nicole is a post-Doc student working on methods for analyzing longitudinal outcomes with missing baseline and time-varying covariates. She currently also writes an R package for implementing a full Bayesian anlysis in such settings under the sequential approach. More info on her GitHub page.
    [GitHub] [twitter]

  • Pedro Manuel Miranda Afonso: Pedro is a PhD student working on extensions of joint models for recurrent event data and spatial correlations with application in cystic fibrosis.

  • Aglina Lika: Aglina is a PhD student working in developing methodology and software for Bayesian multivariate mixed effects models.

  • Katya Mauff: Katya is a PhD student working on novel computational approaches for multivariate joint models with multiple longitudinal outcomes.

  • Floor van Oudenhoven: Floor is a PhD student working in novel applications and methodology of joint models in clinical trials. Her motivation comes from Alzheimer’s disease.

  • Grigorios Papageorgiou: Greg is a PhD student working on adapting joint models to incorporate time-varying interventions during follow-up and assess their performance.
    [webpage] [GitHub] [twitter]

  • Hongchao Qi: Hongchao is a PhD student working on extensions of power priors methodology in lonigitudinal clinical trials.

  • Anirudh Tomer: Anirudh is a PhD student working in developing new methods for personalized screening and active surveilance. He works closely with the PRIAS project.


The major R packages I have developed and currently maintain


I am the coordinator for the following courses at Erasmus MC:

I have been also teaching short-courses in joint modeling in international conferences. A list of recent and upcoming courses

Recent & Upcoming Talks

More Talks

Recent Publications

More Publications

  • A Bayesian joint model for zero-inflated integers and left-truncated event times with a time-varying association: Applications to senior health care

    Details PDF

  • A marginal estimate for the overall treatment effect on a survival outcome within the joint modeling framework

    Details PDF

  • Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes

    Details PDF

  • Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model

    Details PDF

  • Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes

    Details PDF

  • Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach

    Details PDF Slides Project

  • Personalized decision making for biopsies in prostate cancer active surveillance programs

    Details PDF Project

  • Bayesian imputation of time-varying covariates in linear mixed models

    Details PDF

  • Joint models for longitudinal and time-to-event data in a case-cohort design

    Details PDF

  • Personalized schedules for surveillance of low risk prostate cancer patients

    Details PDF Slides Project Supplementary material


I have written the first book on Joint Models for Longitudinal and Survival Data


Inaugural Speech

A trailer of my inaugural address is available here.


A full list of my publications and grants can be found in my CV.